2024/07/10 04:35:10 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] CUDA available: True numpy_random_seed: 1436402125 GPU 0,1,2,3,4,5: NVIDIA GeForce RTX 3090 CUDA_HOME: /data1/tanghao/cuda/cuda-11.8:/data1/tanghao/cuda/cuda-11.8: GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 PyTorch: 2.1.0+cu118 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.8 - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90 - CuDNN 8.9.5 - Built with CuDNN 8.7 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.16.0+cu118 OpenCV: 4.10.0 MMEngine: 0.8.3 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 1436402125 Distributed launcher: pytorch Distributed training: True GPU number: 6 ------------------------------------------------------------ 2024/07/10 04:35:12 - mmengine - INFO - Config: auto_scale_lr = dict(base_batch_size=24) backend_args = None base_img_size = 1120 custom_hooks = [ dict( ema_type='ExpMomentumEMA', momentum=0.0002, priority=49, type='EMAHook', update_buffers=True), dict( switch_epoch=100000, switch_pipeline=[ dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ], type='PipelineSwitchHook'), ] default_hooks = dict( checkpoint=dict( by_epoch=False, interval=1000, max_keep_ckpts=1, type='CheckpointHook'), logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), sampler_seed=dict(type='DistSamplerSeedHook'), timer=dict(type='IterTimerHook'), visualization=dict(type='DetVisualizationHook')) default_scope = 'mmdet' det_cfgs = dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=2322, samples_grids_eachwin=10) det_test_pipeline = [ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 1120, 1120, ), type='Resize'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=2322, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=2240, num_classes=80, num_vocal=2322), task_name='detection'), type='AddMetaInfo'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ] det_train_pipeline = [ dict(max_num_pasted=100, type='CopyPaste'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ] det_train_pipeline_stage2 = [ dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ] env_cfg = dict( cudnn_benchmark=True, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) launcher = 'pytorch' load_from = None load_pipeline = [ dict(backend_args=None, type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=2322, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=2240, num_classes=80, num_vocal=2322), task_name='detection'), type='AddMetaInfo'), dict(prob=0.5, type='RandomFlip'), dict( transforms=[ [ dict( keep_ratio=False, scales=[ ( 1120, 1120, ), ], type='RandomChoiceResize'), ], [ dict( keep_ratio=True, scales=[ ( 400, 4200, ), ( 500, 4200, ), ( 600, 4200, ), ], type='RandomChoiceResize'), dict( allow_negative_crop=True, crop_size=( 384, 600, ), crop_type='absolute_range', type='RandomCrop'), dict( keep_ratio=False, scales=[ ( 1120, 1120, ), ], type='RandomChoiceResize'), ], ], type='RandomChoice'), dict(min_gt_bbox_wh=( 1e-05, 1e-05, ), type='FilterAnnotations'), ] log_level = 'INFO' log_processor = dict(by_epoch=False, type='LogProcessor', window_size=4000) max_iters = 120000 model = dict( backbone=dict( arch='base', drop_path_rate=0.1, img_size=1120, init_cfg=dict( checkpoint='./sam-base-repeat-10.pth', prefix='backbone.', type='Pretrained'), new_more_layers=[ 'win', 'win', 'win', 'win', 'win', 'win', ], out_channels=0, out_type='featmap', patch_size=16, text_cfg=dict( hidden_size=768, pretrain_path='./blip_embed.pt', type='bert-base'), type='ViTGiTPromptBeta', use_abs_pos=True, use_checkpoints=True, use_rel_pos=True, window_size=14), data_preprocessor=dict( bgr_to_rgb=True, mean=[ 123.675, 116.28, 103.53, ], pad_seg=True, pad_size_divisor=224, seg_pad_value=255, std=[ 58.395, 57.12, 57.375, ], type='GeneralDataPreprocessor'), head_list=dict( detection_head=dict( nms=dict(iou_threshold=0.5, type='soft_nms'), repeat_times=3, test_cfg=dict(max_per_img=100), train_cfg=dict( assigner=dict( match_costs=[ dict( box_format='xywh', type='PointsL1Cost', weight=5.0), ], type='HungarianAssigner')), type='GiTDetHeadPromptBeta')), mean_layes=[ 12, 13, 14, 15, 16, 17, ], mean_output=True, support_tasks=[ 'detection', 'semantic_segmentation', 'instance_segmentation', 'caption', 'grounding', ], tokenizer=dict(name_or_path='bert-base-uncased', type='BlipTokenizer'), type='GiTPromptBeta', use_checkpoints=True) optim_wrapper = dict( clip_grad=dict(max_norm=0.1, norm_type=2), dtype='bfloat16', optimizer=dict(lr=0.0002, type='AdamW', weight_decay=0.05), paramwise_cfg=dict( custom_keys=dict({ 'backbone': dict(lr_mult=0.1), 'backbone.embed': dict(lr_mult=1.0), 'backbone.layers.10': dict(lr_mult=0.7429), 'backbone.layers.11': dict(lr_mult=0.8714), 'backbone.layers.12': dict(lr_mult=1.0), 'backbone.layers.13': dict(lr_mult=1.0), 'backbone.layers.14': dict(lr_mult=1.0), 'backbone.layers.15': dict(lr_mult=1.0), 'backbone.layers.16': dict(lr_mult=1.0), 'backbone.layers.17': dict(lr_mult=1.0), 'backbone.layers.6': dict(lr_mult=0.2286), 'backbone.layers.7': dict(lr_mult=0.3571), 'backbone.layers.8': dict(lr_mult=0.4858), 'backbone.layers.9': dict(lr_mult=0.6143), 'reference_points': dict(lr_mult=0.1), 'sampling_offsets': dict(lr_mult=0.1) })), type='AmpOptimWrapper') param_scheduler = [ dict( T_max=120000, begin=0, by_epoch=False, end=120000, eta_min=2e-06, type='CosineAnnealingLR'), ] pretrained = './sam-base-repeat-10.pth' resume = False test_cfg = dict(type='TestLoop') test_dataloader = dict( batch_size=1, dataset=dict( ann_file='annotations/instances_val2017.json', backend_args=None, data_prefix=dict(img='val2017/'), data_root='data/coco/', pipeline=[ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 1120, 1120, ), type='Resize'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=2322, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=2240, num_classes=80, num_vocal=2322), task_name='detection'), type='AddMetaInfo'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ], return_classes=True, test_mode=True, type='CocoDataset'), drop_last=False, num_workers=3, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) test_evaluator = dict( ann_file='data/coco/annotations/instances_val2017.json', backend_args=None, format_only=False, metric='bbox', type='CocoMetric') test_pipeline = [ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 1120, 1120, ), type='Resize'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=2322, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=2240, num_classes=80, num_vocal=2322), task_name='detection'), type='AddMetaInfo'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ] train_cfg = dict( max_iters=120000, type='IterBasedTrainLoop', val_interval=5000) train_dataloader = dict( batch_sampler=None, batch_size=4, dataset=dict( datasets=[ dict( dataset=dict( ann_file='annotations/instances_train2017.json', backend_args=None, data_prefix=dict(img='train2017/'), data_root='data/coco/', filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=[ dict(backend_args=None, type='LoadImageFromFile'), dict( type='LoadAnnotations', with_bbox=True, with_mask=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=2322, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=2240, num_classes=80, num_vocal=2322), task_name='detection'), type='AddMetaInfo'), dict(prob=0.5, type='RandomFlip'), dict( transforms=[ [ dict( keep_ratio=False, scales=[ ( 1120, 1120, ), ], type='RandomChoiceResize'), ], [ dict( keep_ratio=True, scales=[ ( 400, 4200, ), ( 500, 4200, ), ( 600, 4200, ), ], type='RandomChoiceResize'), dict( allow_negative_crop=True, crop_size=( 384, 600, ), crop_type='absolute_range', type='RandomCrop'), dict( keep_ratio=False, scales=[ ( 1120, 1120, ), ], type='RandomChoiceResize'), ], ], type='RandomChoice'), dict( min_gt_bbox_wh=( 1e-05, 1e-05, ), type='FilterAnnotations'), ], return_classes=True, type='CocoDataset'), pipeline=[ dict(max_num_pasted=100, type='CopyPaste'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ], type='MultiImageMixDataset'), ], ignore_keys=[ 'reduce_zero_label', 'label_map', 'classes', 'palette', ], type='ConcatDataset'), num_workers=4, persistent_workers=True, sampler=dict( batch_size=4, if_group=[ True, ], shuffle=True, source_ratio=[ 1.0, ], type='GroupMultiSourceNonMixedSampler')) tta_model = dict(type='SegTTAModel') val_cfg = dict(type='ValLoop') val_dataloader = dict( batch_size=1, dataset=dict( ann_file='annotations/instances_val2017.json', backend_args=None, data_prefix=dict(img='val2017/'), data_root='data/coco/', pipeline=[ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=False, scale=( 1120, 1120, ), type='Resize'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_dict=dict( git_cfg=dict( global_only_image=True, grid_interpolate=True, grid_resolution_perwin=[ 5, 5, ], max_length=30, mode='detection', num_vocal=2322, samples_grids_eachwin=10), head_cfg=dict( max_length=30, num_bins=2240, num_classes=80, num_vocal=2322), task_name='detection'), type='AddMetaInfo'), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'task_name', 'head_cfg', 'git_cfg', ), type='PackDetInputs'), ], return_classes=True, test_mode=True, type='CocoDataset'), drop_last=False, num_workers=3, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) val_evaluator = dict( ann_file='data/coco/annotations/instances_val2017.json', backend_args=None, format_only=False, metric='bbox', type='CocoMetric') vis_backends = [ dict(type='LocalVisBackend'), ] visualizer = dict( name='visualizer', type='DetLocalVisualizer', vis_backends=[ dict(type='LocalVisBackend'), ]) work_dir = './work_dirs/single_detection_base_1120_prompt_beta' 2024/07/10 04:36:17 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (BELOW_NORMAL) LoggerHook -------------------- after_load_checkpoint: (49 ) EMAHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook (NORMAL ) PipelineSwitchHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_val_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_save_checkpoint: (49 ) EMAHook -------------------- after_train: (VERY_HIGH ) RuntimeInfoHook (VERY_LOW ) CheckpointHook -------------------- before_test: (VERY_HIGH ) RuntimeInfoHook -------------------- before_test_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_test: (VERY_HIGH ) RuntimeInfoHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.pos_embed:lr=2e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.pos_embed:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.pos_embed:lr_mult=0.1 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.weight:lr=2e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.weight:lr_mult=0.1 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.bias:lr=2e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.bias:lr_mult=0.1 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.word_embeddings.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.word_embeddings.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.word_embeddings.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.position_embeddings.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.position_embeddings.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.position_embeddings.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.embed.LayerNorm.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.weight:lr=2e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.weight:lr_mult=0.1 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.bias:lr=2e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.ln1.bias:lr_mult=0.1 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_h:lr=2e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_h:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_h:lr_mult=0.1 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_w:lr=2e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_w:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.rel_pos_w:lr_mult=0.1 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.weight:lr=2e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.weight:lr_mult=0.1 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.bias:lr=2e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.qkv.bias:lr_mult=0.1 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.proj.weight:lr=2e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.proj.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.0.attn.proj.weight:lr_mult=0.1 2024/07/10 04:37:25 - mmengine - INFO - 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mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.weight:lr=9.716000000000001e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.weight:lr_mult=0.4858 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.bias:lr=9.716000000000001e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.0.0.bias:lr_mult=0.4858 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.1.weight:lr=9.716000000000001e-05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.8.ffn.layers.1.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- 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mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_h:lr_mult=0.7429 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_w:lr=0.00014858000000000002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_w:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.rel_pos_w:lr_mult=0.7429 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.weight:lr=0.00014858000000000002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.weight:lr_mult=0.7429 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.attn.qkv.bias:lr=0.00014858000000000002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- 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mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.weight:lr_mult=0.7429 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.bias:lr=0.00014858000000000002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.ln2.bias:lr_mult=0.7429 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.ffn.layers.0.0.weight:lr=0.00014858000000000002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.10.ffn.layers.0.0.weight:lr_mult=0.7429 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- 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backbone.layers.16.attn.qkv.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.qkv.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.qkv.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.attn.proj.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ln2.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.0.0.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.16.ffn.layers.1.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln1.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.qkv.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.attn.proj.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ln2.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.0.0.bias:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.weight:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.weight:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.weight:lr_mult=1.0 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.bias:lr=0.0002 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.bias:weight_decay=0.05 2024/07/10 04:37:25 - mmengine - INFO - paramwise_options -- backbone.layers.17.ffn.layers.1.bias:lr_mult=1.0 2024/07/10 04:37:33 - mmengine - INFO - load backbone. in model from: ./sam-base-repeat-10.pth 2024/07/10 04:37:33 - mmengine - INFO - Resize the pos_embed shape from torch.Size([1, 64, 64, 768]) to torch.Size([1, 70, 70, 768]). 2024/07/10 04:37:34 - mmengine - INFO - Resize the layers.2.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([139, 64]) 2024/07/10 04:37:34 - mmengine - INFO - Resize the layers.2.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([139, 64]) 2024/07/10 04:37:34 - mmengine - INFO - Resize the layers.5.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([139, 64]) 2024/07/10 04:37:34 - mmengine - INFO - Resize the layers.5.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([139, 64]) 2024/07/10 04:37:34 - mmengine - INFO - Resize the layers.8.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([139, 64]) 2024/07/10 04:37:34 - mmengine - INFO - Resize the layers.8.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([139, 64]) 2024/07/10 04:37:34 - mmengine - INFO - Resize the layers.11.attn.rel_pos_h from torch.Size([127, 64]) to torch.Size([139, 64]) 2024/07/10 04:37:34 - mmengine - INFO - Resize the layers.11.attn.rel_pos_w from torch.Size([127, 64]) to torch.Size([139, 64]) 2024/07/10 04:37:34 - mmengine - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: channel_reduction.0.weight, channel_reduction.1.weight, channel_reduction.1.bias, channel_reduction.2.weight, channel_reduction.3.weight, channel_reduction.3.bias missing keys in source state_dict: embed.position_ids, embed.word_embeddings.weight, embed.position_embeddings.weight, embed.LayerNorm.weight, embed.LayerNorm.bias Name of parameter - Initialization information backbone.pos_embed - torch.Size([1, 70, 70, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.patch_embed.projection.weight - torch.Size([768, 3, 16, 16]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.patch_embed.projection.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.embed.word_embeddings.weight - torch.Size([30524, 768]): The value is the same before and after calling `init_weights` of GiTPromptBeta backbone.embed.position_embeddings.weight - torch.Size([512, 768]): The value is the same before and after calling `init_weights` of GiTPromptBeta backbone.embed.LayerNorm.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of GiTPromptBeta backbone.embed.LayerNorm.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of GiTPromptBeta backbone.layers.0.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.0.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.1.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.rel_pos_h - torch.Size([139, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.rel_pos_w - torch.Size([139, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.2.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.3.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.4.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.rel_pos_h - torch.Size([139, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.rel_pos_w - torch.Size([139, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.5.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.6.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.7.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.rel_pos_h - torch.Size([139, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.rel_pos_w - torch.Size([139, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.8.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.9.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.10.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.rel_pos_h - torch.Size([139, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.rel_pos_w - torch.Size([139, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.11.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.12.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.13.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.14.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.15.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.16.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln1.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.rel_pos_h - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.rel_pos_w - torch.Size([27, 64]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.qkv.weight - torch.Size([2304, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.qkv.bias - torch.Size([2304]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.proj.weight - torch.Size([768, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.attn.proj.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln2.weight - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ln2.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.0.0.weight - torch.Size([3072, 768]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.0.0.bias - torch.Size([3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.1.weight - torch.Size([768, 3072]): PretrainedInit: load from ./sam-base-repeat-10.pth backbone.layers.17.ffn.layers.1.bias - torch.Size([768]): PretrainedInit: load from ./sam-base-repeat-10.pth 2024/07/10 04:37:34 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2024/07/10 04:37:34 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2024/07/10 04:37:34 - mmengine - INFO - Checkpoints will be saved to /home/tanghao/mpi/GiT-main/work_dirs/single_detection_base_1120_prompt_beta. 2024/07/10 04:39:57 - mmengine - INFO - Iter(train) [ 50/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 3 days, 23:29:04 time: 2.8657 data_time: 0.1479 memory: 14094 grad_norm: 7.0190 loss: 1.7180 detection_loss_cls: 1.7180 2024/07/10 04:42:17 - mmengine - INFO - Iter(train) [ 100/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 3 days, 22:23:42 time: 2.8342 data_time: 0.1390 memory: 14094 grad_norm: 4.1669 loss: 1.3576 detection_loss_cls: 1.3576 2024/07/10 04:44:38 - mmengine - INFO - Iter(train) [ 150/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 3 days, 22:13:37 time: 2.8304 data_time: 0.1415 memory: 14094 grad_norm: 3.0700 loss: 1.2201 detection_loss_cls: 1.2201 2024/07/10 04:46:59 - mmengine - INFO - Iter(train) [ 200/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 3 days, 21:57:54 time: 2.8237 data_time: 0.1458 memory: 14094 grad_norm: 2.5286 loss: 1.1579 detection_loss_cls: 1.1579 2024/07/10 04:49:19 - mmengine - INFO - Iter(train) [ 250/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 3 days, 21:53:04 time: 2.8224 data_time: 0.1514 memory: 14094 grad_norm: 2.1959 loss: 1.1244 detection_loss_cls: 1.1244 2024/07/10 04:51:39 - mmengine - INFO - Iter(train) [ 300/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 3 days, 21:43:07 time: 2.8186 data_time: 0.1506 memory: 14094 grad_norm: 1.9626 loss: 1.0824 detection_loss_cls: 1.0824 2024/07/10 04:53:59 - mmengine - INFO - Iter(train) [ 350/120000] base_lr: 2.0000e-04 lr: 2.0000e-05 eta: 3 days, 21:33:29 time: 2.8150 data_time: 0.1501 memory: 14094 grad_norm: 1.7943 loss: 1.0511 detection_loss_cls: 1.0511 2024/07/10 04:56:20 - mmengine - INFO - Iter(train) [ 400/120000] base_lr: 1.9999e-04 lr: 2.0000e-05 eta: 3 days, 21:30:10 time: 2.8145 data_time: 0.1507 memory: 14094 grad_norm: 1.6640 loss: 1.0264 detection_loss_cls: 1.0264 2024/07/10 04:58:40 - 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mmengine - INFO - Iter(train) [ 700/120000] base_lr: 1.9998e-04 lr: 1.9998e-05 eta: 3 days, 21:04:23 time: 2.8086 data_time: 0.1533 memory: 14094 grad_norm: 1.2669 loss: 0.9664 detection_loss_cls: 0.9664 2024/07/10 05:12:40 - mmengine - INFO - Iter(train) [ 750/120000] base_lr: 1.9998e-04 lr: 1.9998e-05 eta: 3 days, 20:59:58 time: 2.8075 data_time: 0.1520 memory: 14094 grad_norm: 1.2285 loss: 0.9534 detection_loss_cls: 0.9534 2024/07/10 05:14:59 - mmengine - INFO - Iter(train) [ 800/120000] base_lr: 1.9998e-04 lr: 1.9998e-05 eta: 3 days, 20:56:29 time: 2.8070 data_time: 0.1522 memory: 14094 grad_norm: 1.1964 loss: 0.9461 detection_loss_cls: 0.9461 2024/07/10 05:17:19 - mmengine - INFO - Iter(train) [ 850/120000] base_lr: 1.9998e-04 lr: 1.9998e-05 eta: 3 days, 20:52:33 time: 2.8062 data_time: 0.1515 memory: 14094 grad_norm: 1.1676 loss: 0.9370 detection_loss_cls: 0.9370 2024/07/10 05:19:39 - mmengine - INFO - Iter(train) [ 900/120000] base_lr: 1.9997e-04 lr: 1.9998e-05 eta: 3 days, 20:48:13 time: 2.8052 data_time: 0.1515 memory: 14094 grad_norm: 1.1413 loss: 0.9299 detection_loss_cls: 0.9299 2024/07/10 05:21:58 - 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mmengine - INFO - Saving checkpoint at 3000 iterations 2024/07/10 07:00:29 - mmengine - INFO - Iter(train) [ 3050/120000] base_lr: 1.9968e-04 lr: 1.9971e-05 eta: 3 days, 19:19:50 time: 2.8114 data_time: 0.1568 memory: 14094 grad_norm: 0.8134 loss: 0.7954 detection_loss_cls: 0.7954 2024/07/10 07:02:49 - mmengine - INFO - Iter(train) [ 3100/120000] base_lr: 1.9967e-04 lr: 1.9970e-05 eta: 3 days, 19:17:18 time: 2.8113 data_time: 0.1570 memory: 14094 grad_norm: 0.8108 loss: 0.7949 detection_loss_cls: 0.7949 2024/07/10 07:05:10 - mmengine - INFO - Iter(train) [ 3150/120000] base_lr: 1.9966e-04 lr: 1.9969e-05 eta: 3 days, 19:15:22 time: 2.8115 data_time: 0.1571 memory: 14094 grad_norm: 0.8088 loss: 0.7935 detection_loss_cls: 0.7935 2024/07/10 07:07:31 - mmengine - INFO - Iter(train) [ 3200/120000] base_lr: 1.9965e-04 lr: 1.9968e-05 eta: 3 days, 19:12:56 time: 2.8114 data_time: 0.1568 memory: 14094 grad_norm: 0.8063 loss: 0.7914 detection_loss_cls: 0.7914 2024/07/10 07:09:51 - mmengine - INFO - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 07:44:50 - mmengine - INFO - Iter(train) [ 4000/120000] base_lr: 1.9946e-04 lr: 1.9951e-05 eta: 3 days, 18:30:28 time: 2.8089 data_time: 0.1543 memory: 14094 grad_norm: 0.7763 loss: 0.7684 detection_loss_cls: 0.7684 2024/07/10 07:44:50 - mmengine - INFO - Saving checkpoint at 4000 iterations 2024/07/10 07:47:18 - mmengine - INFO - Iter(train) [ 4050/120000] base_lr: 1.9944e-04 lr: 1.9949e-05 eta: 3 days, 18:31:47 time: 2.8101 data_time: 0.1563 memory: 14094 grad_norm: 0.6965 loss: 0.7554 detection_loss_cls: 0.7554 2024/07/10 07:49:38 - mmengine - INFO - Iter(train) [ 4100/120000] base_lr: 1.9943e-04 lr: 1.9948e-05 eta: 3 days, 18:29:14 time: 2.8101 data_time: 0.1565 memory: 14094 grad_norm: 0.6882 loss: 0.7513 detection_loss_cls: 0.7513 2024/07/10 07:51:58 - mmengine - INFO - Iter(train) [ 4150/120000] base_lr: 1.9942e-04 lr: 1.9947e-05 eta: 3 days, 18:26:30 time: 2.8097 data_time: 0.1563 memory: 14094 grad_norm: 0.6854 loss: 0.7477 detection_loss_cls: 0.7477 2024/07/10 07:54:18 - mmengine - INFO - Iter(train) [ 4200/120000] base_lr: 1.9940e-04 lr: 1.9946e-05 eta: 3 days, 18:24:03 time: 2.8097 data_time: 0.1561 memory: 14094 grad_norm: 0.6824 loss: 0.7438 detection_loss_cls: 0.7438 2024/07/10 07:56:39 - mmengine - INFO - Iter(train) [ 4250/120000] base_lr: 1.9939e-04 lr: 1.9944e-05 eta: 3 days, 18:21:59 time: 2.8098 data_time: 0.1559 memory: 14094 grad_norm: 0.6798 loss: 0.7399 detection_loss_cls: 0.7399 2024/07/10 07:58:59 - mmengine - INFO - Iter(train) [ 4300/120000] base_lr: 1.9937e-04 lr: 1.9943e-05 eta: 3 days, 18:19:32 time: 2.8099 data_time: 0.1560 memory: 14094 grad_norm: 0.6779 loss: 0.7368 detection_loss_cls: 0.7368 2024/07/10 08:01:20 - mmengine - INFO - Iter(train) [ 4350/120000] base_lr: 1.9936e-04 lr: 1.9942e-05 eta: 3 days, 18:17:19 time: 2.8102 data_time: 0.1560 memory: 14094 grad_norm: 0.6763 loss: 0.7346 detection_loss_cls: 0.7346 2024/07/10 08:03:41 - mmengine - INFO - Iter(train) [ 4400/120000] base_lr: 1.9934e-04 lr: 1.9940e-05 eta: 3 days, 18:14:55 time: 2.8101 data_time: 0.1559 memory: 14094 grad_norm: 0.6750 loss: 0.7324 detection_loss_cls: 0.7324 2024/07/10 08:06:00 - mmengine - INFO - Iter(train) [ 4450/120000] base_lr: 1.9933e-04 lr: 1.9939e-05 eta: 3 days, 18:12:12 time: 2.8099 data_time: 0.1558 memory: 14094 grad_norm: 0.6733 loss: 0.7291 detection_loss_cls: 0.7291 2024/07/10 08:08:20 - mmengine - INFO - Iter(train) [ 4500/120000] base_lr: 1.9931e-04 lr: 1.9938e-05 eta: 3 days, 18:09:35 time: 2.8098 data_time: 0.1558 memory: 14094 grad_norm: 0.6723 loss: 0.7268 detection_loss_cls: 0.7268 2024/07/10 08:10:42 - mmengine - INFO - Iter(train) [ 4550/120000] base_lr: 1.9930e-04 lr: 1.9936e-05 eta: 3 days, 18:07:42 time: 2.8103 data_time: 0.1560 memory: 14094 grad_norm: 0.6713 loss: 0.7252 detection_loss_cls: 0.7252 2024/07/10 08:13:04 - mmengine - INFO - Iter(train) [ 4600/120000] base_lr: 1.9928e-04 lr: 1.9935e-05 eta: 3 days, 18:06:01 time: 2.8108 data_time: 0.1555 memory: 14094 grad_norm: 0.6703 loss: 0.7216 detection_loss_cls: 0.7216 2024/07/10 08:15:25 - mmengine - INFO - Iter(train) [ 4650/120000] base_lr: 1.9927e-04 lr: 1.9933e-05 eta: 3 days, 18:03:51 time: 2.8110 data_time: 0.1556 memory: 14094 grad_norm: 0.6691 loss: 0.7196 detection_loss_cls: 0.7196 2024/07/10 08:17:45 - mmengine - INFO - Iter(train) [ 4700/120000] base_lr: 1.9925e-04 lr: 1.9932e-05 eta: 3 days, 18:01:34 time: 2.8113 data_time: 0.1555 memory: 14094 grad_norm: 0.6686 loss: 0.7167 detection_loss_cls: 0.7167 2024/07/10 08:20:05 - mmengine - INFO - Iter(train) [ 4750/120000] base_lr: 1.9924e-04 lr: 1.9931e-05 eta: 3 days, 17:58:49 time: 2.8113 data_time: 0.1556 memory: 14094 grad_norm: 0.6681 loss: 0.7151 detection_loss_cls: 0.7151 2024/07/10 08:22:26 - mmengine - INFO - Iter(train) [ 4800/120000] base_lr: 1.9922e-04 lr: 1.9929e-05 eta: 3 days, 17:56:42 time: 2.8116 data_time: 0.1555 memory: 14094 grad_norm: 0.6675 loss: 0.7128 detection_loss_cls: 0.7128 2024/07/10 08:24:47 - mmengine - INFO - Iter(train) [ 4850/120000] base_lr: 1.9920e-04 lr: 1.9928e-05 eta: 3 days, 17:54:30 time: 2.8119 data_time: 0.1555 memory: 14094 grad_norm: 0.6669 loss: 0.7110 detection_loss_cls: 0.7110 2024/07/10 08:27:07 - mmengine - INFO - Iter(train) [ 4900/120000] base_lr: 1.9919e-04 lr: 1.9926e-05 eta: 3 days, 17:52:00 time: 2.8120 data_time: 0.1553 memory: 14094 grad_norm: 0.6664 loss: 0.7085 detection_loss_cls: 0.7085 2024/07/10 08:29:28 - mmengine - INFO - Iter(train) [ 4950/120000] base_lr: 1.9917e-04 lr: 1.9925e-05 eta: 3 days, 17:49:43 time: 2.8124 data_time: 0.1553 memory: 14094 grad_norm: 0.6662 loss: 0.7063 detection_loss_cls: 0.7063 2024/07/10 08:31:49 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 08:31:49 - mmengine - INFO - Iter(train) [ 5000/120000] base_lr: 1.9915e-04 lr: 1.9923e-05 eta: 3 days, 17:47:36 time: 2.8128 data_time: 0.1554 memory: 14094 grad_norm: 0.6659 loss: 0.7049 detection_loss_cls: 0.7049 2024/07/10 08:31:49 - mmengine - INFO - Saving checkpoint at 5000 iterations 2024/07/10 08:32:26 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:21 time: 0.4097 data_time: 0.0100 memory: 5708 2024/07/10 08:32:46 - mmengine - INFO - Iter(val) [100/834] eta: 0:04:55 time: 0.4026 data_time: 0.0072 memory: 5707 2024/07/10 08:33:06 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:35 time: 0.4024 data_time: 0.0063 memory: 5706 2024/07/10 08:33:26 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:14 time: 0.4018 data_time: 0.0059 memory: 5706 2024/07/10 08:33:46 - mmengine - INFO - Iter(val) [250/834] eta: 0:03:54 time: 0.4014 data_time: 0.0056 memory: 5706 2024/07/10 08:34:06 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:33 time: 0.4000 data_time: 0.0054 memory: 5706 2024/07/10 08:34:26 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:13 time: 0.4003 data_time: 0.0053 memory: 5706 2024/07/10 08:34:46 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:53 time: 0.4005 data_time: 0.0052 memory: 5706 2024/07/10 08:35:06 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:33 time: 0.4000 data_time: 0.0051 memory: 5706 2024/07/10 08:35:26 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:13 time: 0.4003 data_time: 0.0051 memory: 5706 2024/07/10 08:35:46 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:53 time: 0.3998 data_time: 0.0050 memory: 5706 2024/07/10 08:36:06 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:33 time: 0.4001 data_time: 0.0050 memory: 5706 2024/07/10 08:36:26 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:13 time: 0.3999 data_time: 0.0049 memory: 5706 2024/07/10 08:36:46 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:53 time: 0.3998 data_time: 0.0049 memory: 5706 2024/07/10 08:37:05 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:33 time: 0.3995 data_time: 0.0049 memory: 5706 2024/07/10 08:37:25 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.3994 data_time: 0.0049 memory: 5706 2024/07/10 08:37:42 - mmengine - INFO - Evaluating bbox... 2024/07/10 08:38:08 - mmengine - INFO - bbox_mAP_copypaste: 0.259 0.399 0.279 0.114 0.287 0.356 2024/07/10 08:38:08 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.2590 coco/bbox_mAP_50: 0.3990 coco/bbox_mAP_75: 0.2790 coco/bbox_mAP_s: 0.1140 coco/bbox_mAP_m: 0.2870 coco/bbox_mAP_l: 0.3560 data_time: 0.0048 time: 0.3994 2024/07/10 08:40:28 - mmengine - INFO - Iter(train) [ 5050/120000] base_lr: 1.9914e-04 lr: 1.9921e-05 eta: 3 days, 17:56:00 time: 2.8182 data_time: 0.1608 memory: 14139 grad_norm: 0.6653 loss: 0.7027 detection_loss_cls: 0.7027 2024/07/10 08:42:49 - mmengine - INFO - Iter(train) [ 5100/120000] base_lr: 1.9912e-04 lr: 1.9920e-05 eta: 3 days, 17:53:50 time: 2.8183 data_time: 0.1614 memory: 14139 grad_norm: 0.6653 loss: 0.7014 detection_loss_cls: 0.7014 2024/07/10 08:45:09 - mmengine - INFO - Iter(train) [ 5150/120000] base_lr: 1.9910e-04 lr: 1.9918e-05 eta: 3 days, 17:51:12 time: 2.8185 data_time: 0.1616 memory: 14139 grad_norm: 0.6648 loss: 0.6994 detection_loss_cls: 0.6994 2024/07/10 08:47:30 - mmengine - INFO - Iter(train) [ 5200/120000] base_lr: 1.9908e-04 lr: 1.9917e-05 eta: 3 days, 17:48:54 time: 2.8188 data_time: 0.1620 memory: 14139 grad_norm: 0.6642 loss: 0.6976 detection_loss_cls: 0.6976 2024/07/10 08:49:50 - mmengine - INFO - Iter(train) [ 5250/120000] base_lr: 1.9907e-04 lr: 1.9915e-05 eta: 3 days, 17:46:10 time: 2.8189 data_time: 0.1624 memory: 14139 grad_norm: 0.6643 loss: 0.6956 detection_loss_cls: 0.6956 2024/07/10 08:52:10 - mmengine - INFO - Iter(train) [ 5300/120000] base_lr: 1.9905e-04 lr: 1.9914e-05 eta: 3 days, 17:43:46 time: 2.8191 data_time: 0.1631 memory: 14139 grad_norm: 0.6638 loss: 0.6939 detection_loss_cls: 0.6939 2024/07/10 08:54:30 - mmengine - INFO - Iter(train) [ 5350/120000] base_lr: 1.9903e-04 lr: 1.9912e-05 eta: 3 days, 17:41:08 time: 2.8191 data_time: 0.1635 memory: 14139 grad_norm: 0.6637 loss: 0.6921 detection_loss_cls: 0.6921 2024/07/10 08:56:51 - mmengine - INFO - Iter(train) [ 5400/120000] base_lr: 1.9901e-04 lr: 1.9910e-05 eta: 3 days, 17:38:45 time: 2.8190 data_time: 0.1638 memory: 14139 grad_norm: 0.6634 loss: 0.6908 detection_loss_cls: 0.6908 2024/07/10 08:59:12 - mmengine - INFO - Iter(train) [ 5450/120000] base_lr: 1.9899e-04 lr: 1.9909e-05 eta: 3 days, 17:36:24 time: 2.8193 data_time: 0.1640 memory: 14139 grad_norm: 0.6630 loss: 0.6894 detection_loss_cls: 0.6894 2024/07/10 09:01:34 - mmengine - INFO - Iter(train) [ 5500/120000] base_lr: 1.9898e-04 lr: 1.9907e-05 eta: 3 days, 17:34:26 time: 2.8197 data_time: 0.1644 memory: 14139 grad_norm: 0.6626 loss: 0.6880 detection_loss_cls: 0.6880 2024/07/10 09:03:54 - mmengine - INFO - Iter(train) [ 5550/120000] base_lr: 1.9896e-04 lr: 1.9905e-05 eta: 3 days, 17:31:57 time: 2.8200 data_time: 0.1649 memory: 14139 grad_norm: 0.6619 loss: 0.6865 detection_loss_cls: 0.6865 2024/07/10 09:06:14 - mmengine - INFO - Iter(train) [ 5600/120000] base_lr: 1.9894e-04 lr: 1.9903e-05 eta: 3 days, 17:29:07 time: 2.8199 data_time: 0.1653 memory: 14139 grad_norm: 0.6618 loss: 0.6851 detection_loss_cls: 0.6851 2024/07/10 09:08:34 - mmengine - INFO - Iter(train) [ 5650/120000] base_lr: 1.9892e-04 lr: 1.9902e-05 eta: 3 days, 17:26:39 time: 2.8202 data_time: 0.1659 memory: 14139 grad_norm: 0.6618 loss: 0.6846 detection_loss_cls: 0.6846 2024/07/10 09:10:55 - mmengine - INFO - Iter(train) [ 5700/120000] base_lr: 1.9890e-04 lr: 1.9900e-05 eta: 3 days, 17:24:18 time: 2.8202 data_time: 0.1666 memory: 14139 grad_norm: 0.6613 loss: 0.6836 detection_loss_cls: 0.6836 2024/07/10 09:13:15 - mmengine - INFO - Iter(train) [ 5750/120000] base_lr: 1.9888e-04 lr: 1.9898e-05 eta: 3 days, 17:21:50 time: 2.8204 data_time: 0.1669 memory: 14139 grad_norm: 0.6606 loss: 0.6821 detection_loss_cls: 0.6821 2024/07/10 09:15:36 - mmengine - INFO - Iter(train) [ 5800/120000] base_lr: 1.9886e-04 lr: 1.9896e-05 eta: 3 days, 17:19:32 time: 2.8207 data_time: 0.1672 memory: 14139 grad_norm: 0.6607 loss: 0.6799 detection_loss_cls: 0.6799 2024/07/10 09:17:56 - mmengine - INFO - Iter(train) [ 5850/120000] base_lr: 1.9884e-04 lr: 1.9895e-05 eta: 3 days, 17:16:40 time: 2.8206 data_time: 0.1675 memory: 14139 grad_norm: 0.6605 loss: 0.6786 detection_loss_cls: 0.6786 2024/07/10 09:20:14 - mmengine - INFO - Iter(train) [ 5900/120000] base_lr: 1.9882e-04 lr: 1.9893e-05 eta: 3 days, 17:13:38 time: 2.8201 data_time: 0.1679 memory: 14139 grad_norm: 0.6599 loss: 0.6767 detection_loss_cls: 0.6767 2024/07/10 09:22:33 - mmengine - INFO - Iter(train) [ 5950/120000] base_lr: 1.9880e-04 lr: 1.9891e-05 eta: 3 days, 17:10:35 time: 2.8193 data_time: 0.1683 memory: 14139 grad_norm: 0.6596 loss: 0.6750 detection_loss_cls: 0.6750 2024/07/10 09:24:53 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 09:24:53 - mmengine - INFO - Iter(train) [ 6000/120000] base_lr: 1.9878e-04 lr: 1.9889e-05 eta: 3 days, 17:08:06 time: 2.8194 data_time: 0.1688 memory: 14139 grad_norm: 0.6594 loss: 0.6733 detection_loss_cls: 0.6733 2024/07/10 09:24:53 - mmengine - INFO - Saving checkpoint at 6000 iterations 2024/07/10 09:27:20 - mmengine - INFO - Iter(train) [ 6050/120000] base_lr: 1.9876e-04 lr: 1.9887e-05 eta: 3 days, 17:07:48 time: 2.8190 data_time: 0.1693 memory: 14139 grad_norm: 0.6593 loss: 0.6728 detection_loss_cls: 0.6728 2024/07/10 09:29:41 - mmengine - INFO - Iter(train) [ 6100/120000] base_lr: 1.9874e-04 lr: 1.9886e-05 eta: 3 days, 17:05:18 time: 2.8195 data_time: 0.1698 memory: 14139 grad_norm: 0.6592 loss: 0.6713 detection_loss_cls: 0.6713 2024/07/10 09:32:00 - mmengine - INFO - Iter(train) [ 6150/120000] base_lr: 1.9872e-04 lr: 1.9884e-05 eta: 3 days, 17:02:26 time: 2.8190 data_time: 0.1702 memory: 14139 grad_norm: 0.6591 loss: 0.6696 detection_loss_cls: 0.6696 2024/07/10 09:34:21 - mmengine - INFO - Iter(train) [ 6200/120000] base_lr: 1.9870e-04 lr: 1.9882e-05 eta: 3 days, 17:00:20 time: 2.8197 data_time: 0.1708 memory: 14139 grad_norm: 0.6590 loss: 0.6688 detection_loss_cls: 0.6688 2024/07/10 09:36:41 - mmengine - INFO - Iter(train) [ 6250/120000] base_lr: 1.9868e-04 lr: 1.9880e-05 eta: 3 days, 16:57:32 time: 2.8197 data_time: 0.1714 memory: 14139 grad_norm: 0.6587 loss: 0.6683 detection_loss_cls: 0.6683 2024/07/10 09:39:00 - mmengine - INFO - Iter(train) [ 6300/120000] base_lr: 1.9866e-04 lr: 1.9878e-05 eta: 3 days, 16:54:52 time: 2.8198 data_time: 0.1717 memory: 14139 grad_norm: 0.6587 loss: 0.6673 detection_loss_cls: 0.6673 2024/07/10 09:41:20 - mmengine - INFO - Iter(train) [ 6350/120000] base_lr: 1.9864e-04 lr: 1.9876e-05 eta: 3 days, 16:52:05 time: 2.8198 data_time: 0.1718 memory: 14139 grad_norm: 0.6583 loss: 0.6658 detection_loss_cls: 0.6658 2024/07/10 09:43:40 - mmengine - INFO - Iter(train) [ 6400/120000] base_lr: 1.9861e-04 lr: 1.9874e-05 eta: 3 days, 16:49:29 time: 2.8200 data_time: 0.1725 memory: 14139 grad_norm: 0.6582 loss: 0.6650 detection_loss_cls: 0.6650 2024/07/10 09:46:00 - mmengine - INFO - Iter(train) [ 6450/120000] base_lr: 1.9859e-04 lr: 1.9872e-05 eta: 3 days, 16:47:06 time: 2.8200 data_time: 0.1728 memory: 14139 grad_norm: 0.6578 loss: 0.6639 detection_loss_cls: 0.6639 2024/07/10 09:48:21 - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 10:11:39 - mmengine - INFO - Iter(train) [ 7000/120000] base_lr: 1.9834e-04 lr: 1.9849e-05 eta: 3 days, 16:18:51 time: 2.8175 data_time: 0.1757 memory: 14139 grad_norm: 0.6546 loss: 0.6500 detection_loss_cls: 0.6500 2024/07/10 10:11:39 - mmengine - INFO - Saving checkpoint at 7000 iterations 2024/07/10 10:14:07 - mmengine - INFO - Iter(train) [ 7050/120000] base_lr: 1.9832e-04 lr: 1.9847e-05 eta: 3 days, 16:18:19 time: 2.8169 data_time: 0.1761 memory: 14139 grad_norm: 0.6544 loss: 0.6491 detection_loss_cls: 0.6491 2024/07/10 10:16:27 - mmengine - INFO - Iter(train) [ 7100/120000] base_lr: 1.9830e-04 lr: 1.9845e-05 eta: 3 days, 16:15:49 time: 2.8169 data_time: 0.1761 memory: 14139 grad_norm: 0.6543 loss: 0.6467 detection_loss_cls: 0.6467 2024/07/10 10:18:48 - mmengine - INFO - Iter(train) [ 7150/120000] base_lr: 1.9827e-04 lr: 1.9843e-05 eta: 3 days, 16:13:31 time: 2.8168 data_time: 0.1762 memory: 14139 grad_norm: 0.6538 loss: 0.6453 detection_loss_cls: 0.6453 2024/07/10 10:21:09 - 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mmengine - INFO - Iter(train) [ 7950/120000] base_lr: 1.9786e-04 lr: 1.9806e-05 eta: 3 days, 15:34:09 time: 2.8181 data_time: 0.1833 memory: 14139 grad_norm: 0.6531 loss: 0.6308 detection_loss_cls: 0.6308 2024/07/10 10:58:31 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 10:58:31 - mmengine - INFO - Iter(train) [ 8000/120000] base_lr: 1.9784e-04 lr: 1.9803e-05 eta: 3 days, 15:31:31 time: 2.8178 data_time: 0.1840 memory: 14139 grad_norm: 0.6530 loss: 0.6303 detection_loss_cls: 0.6303 2024/07/10 10:58:31 - mmengine - INFO - Saving checkpoint at 8000 iterations 2024/07/10 11:00:58 - mmengine - INFO - Iter(train) [ 8050/120000] base_lr: 1.9781e-04 lr: 1.9801e-05 eta: 3 days, 15:30:38 time: 2.8175 data_time: 0.1844 memory: 14139 grad_norm: 0.6530 loss: 0.6297 detection_loss_cls: 0.6297 2024/07/10 11:03:18 - mmengine - INFO - Iter(train) [ 8100/120000] base_lr: 1.9778e-04 lr: 1.9798e-05 eta: 3 days, 15:28:00 time: 2.8173 data_time: 0.1849 memory: 14139 grad_norm: 0.6532 loss: 0.6286 detection_loss_cls: 0.6286 2024/07/10 11:05:38 - mmengine - INFO - Iter(train) [ 8150/120000] base_lr: 1.9776e-04 lr: 1.9796e-05 eta: 3 days, 15:25:32 time: 2.8174 data_time: 0.1850 memory: 14139 grad_norm: 0.6532 loss: 0.6275 detection_loss_cls: 0.6275 2024/07/10 11:07:58 - mmengine - INFO - Iter(train) [ 8200/120000] base_lr: 1.9773e-04 lr: 1.9793e-05 eta: 3 days, 15:23:00 time: 2.8173 data_time: 0.1850 memory: 14139 grad_norm: 0.6528 loss: 0.6262 detection_loss_cls: 0.6262 2024/07/10 11:10:18 - mmengine - INFO - Iter(train) [ 8250/120000] base_lr: 1.9770e-04 lr: 1.9791e-05 eta: 3 days, 15:20:37 time: 2.8172 data_time: 0.1854 memory: 14139 grad_norm: 0.6526 loss: 0.6259 detection_loss_cls: 0.6259 2024/07/10 11:12:39 - mmengine - INFO - Iter(train) [ 8300/120000] base_lr: 1.9767e-04 lr: 1.9788e-05 eta: 3 days, 15:18:13 time: 2.8172 data_time: 0.1856 memory: 14139 grad_norm: 0.6525 loss: 0.6254 detection_loss_cls: 0.6254 2024/07/10 11:14:58 - mmengine - INFO - Iter(train) [ 8350/120000] base_lr: 1.9764e-04 lr: 1.9786e-05 eta: 3 days, 15:15:41 time: 2.8170 data_time: 0.1857 memory: 14139 grad_norm: 0.6526 loss: 0.6243 detection_loss_cls: 0.6243 2024/07/10 11:17:17 - 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mmengine - INFO - Iter(train) [ 8900/120000] base_lr: 1.9733e-04 lr: 1.9757e-05 eta: 3 days, 14:48:21 time: 2.8153 data_time: 0.1889 memory: 14139 grad_norm: 0.6508 loss: 0.6150 detection_loss_cls: 0.6150 2024/07/10 11:42:57 - mmengine - INFO - Iter(train) [ 8950/120000] base_lr: 1.9730e-04 lr: 1.9754e-05 eta: 3 days, 14:45:38 time: 2.8148 data_time: 0.1893 memory: 14139 grad_norm: 0.6505 loss: 0.6143 detection_loss_cls: 0.6143 2024/07/10 11:45:18 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 11:45:18 - mmengine - INFO - Iter(train) [ 9000/120000] base_lr: 1.9727e-04 lr: 1.9751e-05 eta: 3 days, 14:43:12 time: 2.8146 data_time: 0.1898 memory: 14139 grad_norm: 0.6500 loss: 0.6136 detection_loss_cls: 0.6136 2024/07/10 11:45:18 - mmengine - INFO - Saving checkpoint at 9000 iterations 2024/07/10 11:47:47 - mmengine - INFO - Iter(train) [ 9050/120000] base_lr: 1.9723e-04 lr: 1.9749e-05 eta: 3 days, 14:42:33 time: 2.8096 data_time: 0.1846 memory: 14139 grad_norm: 0.6500 loss: 0.6130 detection_loss_cls: 0.6130 2024/07/10 11:50:07 - 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mmengine - INFO - Iter(train) [ 9850/120000] base_lr: 1.9673e-04 lr: 1.9702e-05 eta: 3 days, 14:03:33 time: 2.8083 data_time: 0.1842 memory: 14139 grad_norm: 0.6474 loss: 0.6056 detection_loss_cls: 0.6056 2024/07/10 12:27:29 - mmengine - INFO - Iter(train) [ 9900/120000] base_lr: 1.9669e-04 lr: 1.9699e-05 eta: 3 days, 14:00:59 time: 2.8086 data_time: 0.1840 memory: 14139 grad_norm: 0.6473 loss: 0.6049 detection_loss_cls: 0.6049 2024/07/10 12:29:49 - mmengine - INFO - Iter(train) [ 9950/120000] base_lr: 1.9666e-04 lr: 1.9696e-05 eta: 3 days, 13:58:34 time: 2.8090 data_time: 0.1842 memory: 14139 grad_norm: 0.6470 loss: 0.6050 detection_loss_cls: 0.6050 2024/07/10 12:32:09 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 12:32:09 - mmengine - INFO - Iter(train) [ 10000/120000] base_lr: 1.9663e-04 lr: 1.9693e-05 eta: 3 days, 13:56:05 time: 2.8088 data_time: 0.1841 memory: 14139 grad_norm: 0.6469 loss: 0.6049 detection_loss_cls: 0.6049 2024/07/10 12:32:09 - mmengine - INFO - Saving checkpoint at 10000 iterations 2024/07/10 12:32:38 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:21 time: 0.4000 data_time: 0.0049 memory: 5706 2024/07/10 12:32:58 - mmengine - INFO - Iter(val) [100/834] eta: 0:04:59 time: 0.4004 data_time: 0.0048 memory: 5706 2024/07/10 12:33:18 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:40 time: 0.4009 data_time: 0.0048 memory: 5706 2024/07/10 12:33:39 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:20 time: 0.4017 data_time: 0.0048 memory: 5706 2024/07/10 12:34:00 - mmengine - INFO - Iter(val) [250/834] eta: 0:03:59 time: 0.4020 data_time: 0.0048 memory: 5706 2024/07/10 12:34:20 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:38 time: 0.4022 data_time: 0.0048 memory: 5706 2024/07/10 12:34:41 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:18 time: 0.4025 data_time: 0.0048 memory: 5706 2024/07/10 12:35:01 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:57 time: 0.4028 data_time: 0.0048 memory: 5706 2024/07/10 12:35:21 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:37 time: 0.4029 data_time: 0.0048 memory: 5706 2024/07/10 12:35:42 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:16 time: 0.4032 data_time: 0.0048 memory: 5706 2024/07/10 12:36:02 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4033 data_time: 0.0048 memory: 5706 2024/07/10 12:36:23 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:35 time: 0.4035 data_time: 0.0048 memory: 5706 2024/07/10 12:36:43 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4037 data_time: 0.0048 memory: 5706 2024/07/10 12:37:03 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4038 data_time: 0.0048 memory: 5706 2024/07/10 12:37:24 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4039 data_time: 0.0048 memory: 5706 2024/07/10 12:37:44 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4039 data_time: 0.0048 memory: 5706 2024/07/10 12:38:00 - mmengine - INFO - Evaluating bbox... 2024/07/10 12:38:26 - mmengine - INFO - bbox_mAP_copypaste: 0.335 0.486 0.364 0.169 0.365 0.458 2024/07/10 12:38:27 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.3350 coco/bbox_mAP_50: 0.4860 coco/bbox_mAP_75: 0.3640 coco/bbox_mAP_s: 0.1690 coco/bbox_mAP_m: 0.3650 coco/bbox_mAP_l: 0.4580 data_time: 0.0047 time: 0.4086 2024/07/10 12:40:47 - mmengine - INFO - Iter(train) [ 10050/120000] base_lr: 1.9659e-04 lr: 1.9690e-05 eta: 3 days, 13:59:00 time: 2.8144 data_time: 0.1895 memory: 14139 grad_norm: 0.6466 loss: 0.6041 detection_loss_cls: 0.6041 2024/07/10 12:43:08 - mmengine - INFO - Iter(train) [ 10100/120000] base_lr: 1.9656e-04 lr: 1.9687e-05 eta: 3 days, 13:56:39 time: 2.8145 data_time: 0.1896 memory: 14139 grad_norm: 0.6461 loss: 0.6036 detection_loss_cls: 0.6036 2024/07/10 12:45:28 - mmengine - INFO - Iter(train) [ 10150/120000] base_lr: 1.9653e-04 lr: 1.9684e-05 eta: 3 days, 13:54:13 time: 2.8148 data_time: 0.1901 memory: 14139 grad_norm: 0.6462 loss: 0.6036 detection_loss_cls: 0.6036 2024/07/10 12:47:49 - mmengine - INFO - Iter(train) [ 10200/120000] base_lr: 1.9649e-04 lr: 1.9681e-05 eta: 3 days, 13:51:45 time: 2.8144 data_time: 0.1898 memory: 14139 grad_norm: 0.6461 loss: 0.6019 detection_loss_cls: 0.6019 2024/07/10 12:50:09 - mmengine - INFO - Iter(train) [ 10250/120000] base_lr: 1.9646e-04 lr: 1.9678e-05 eta: 3 days, 13:49:25 time: 2.8148 data_time: 0.1902 memory: 14139 grad_norm: 0.6457 loss: 0.6014 detection_loss_cls: 0.6014 2024/07/10 12:52:30 - mmengine - INFO - Iter(train) [ 10300/120000] base_lr: 1.9642e-04 lr: 1.9675e-05 eta: 3 days, 13:47:01 time: 2.8150 data_time: 0.1907 memory: 14139 grad_norm: 0.6454 loss: 0.6014 detection_loss_cls: 0.6014 2024/07/10 12:54:49 - mmengine - INFO - Iter(train) [ 10350/120000] base_lr: 1.9639e-04 lr: 1.9672e-05 eta: 3 days, 13:44:25 time: 2.8150 data_time: 0.1910 memory: 14139 grad_norm: 0.6453 loss: 0.6007 detection_loss_cls: 0.6007 2024/07/10 12:57:10 - mmengine - INFO - Iter(train) [ 10400/120000] base_lr: 1.9635e-04 lr: 1.9669e-05 eta: 3 days, 13:42:04 time: 2.8152 data_time: 0.1911 memory: 14139 grad_norm: 0.6451 loss: 0.6000 detection_loss_cls: 0.6000 2024/07/10 12:59:30 - mmengine - INFO - Iter(train) [ 10450/120000] base_lr: 1.9632e-04 lr: 1.9665e-05 eta: 3 days, 13:39:41 time: 2.8152 data_time: 0.1912 memory: 14139 grad_norm: 0.6446 loss: 0.5988 detection_loss_cls: 0.5988 2024/07/10 13:01:50 - mmengine - INFO - Iter(train) [ 10500/120000] base_lr: 1.9628e-04 lr: 1.9662e-05 eta: 3 days, 13:37:11 time: 2.8150 data_time: 0.1914 memory: 14139 grad_norm: 0.6445 loss: 0.5985 detection_loss_cls: 0.5985 2024/07/10 13:04:10 - mmengine - INFO - Iter(train) [ 10550/120000] base_lr: 1.9625e-04 lr: 1.9659e-05 eta: 3 days, 13:34:36 time: 2.8150 data_time: 0.1916 memory: 14139 grad_norm: 0.6444 loss: 0.5977 detection_loss_cls: 0.5977 2024/07/10 13:06:29 - mmengine - INFO - Iter(train) [ 10600/120000] base_lr: 1.9621e-04 lr: 1.9656e-05 eta: 3 days, 13:31:56 time: 2.8148 data_time: 0.1915 memory: 14139 grad_norm: 0.6441 loss: 0.5966 detection_loss_cls: 0.5966 2024/07/10 13:08:50 - mmengine - INFO - Iter(train) [ 10650/120000] base_lr: 1.9618e-04 lr: 1.9653e-05 eta: 3 days, 13:29:39 time: 2.8150 data_time: 0.1913 memory: 14139 grad_norm: 0.6439 loss: 0.5959 detection_loss_cls: 0.5959 2024/07/10 13:11:09 - mmengine - INFO - Iter(train) [ 10700/120000] base_lr: 1.9614e-04 lr: 1.9649e-05 eta: 3 days, 13:27:05 time: 2.8149 data_time: 0.1915 memory: 14139 grad_norm: 0.6437 loss: 0.5957 detection_loss_cls: 0.5957 2024/07/10 13:13:31 - mmengine - INFO - Iter(train) [ 10750/120000] base_lr: 1.9611e-04 lr: 1.9646e-05 eta: 3 days, 13:24:56 time: 2.8154 data_time: 0.1919 memory: 14139 grad_norm: 0.6433 loss: 0.5958 detection_loss_cls: 0.5958 2024/07/10 13:15:52 - mmengine - INFO - Iter(train) [ 10800/120000] base_lr: 1.9607e-04 lr: 1.9643e-05 eta: 3 days, 13:22:36 time: 2.8159 data_time: 0.1925 memory: 14139 grad_norm: 0.6432 loss: 0.5963 detection_loss_cls: 0.5963 2024/07/10 13:18:11 - mmengine - INFO - Iter(train) [ 10850/120000] base_lr: 1.9603e-04 lr: 1.9639e-05 eta: 3 days, 13:20:03 time: 2.8158 data_time: 0.1928 memory: 14139 grad_norm: 0.6429 loss: 0.5962 detection_loss_cls: 0.5962 2024/07/10 13:20:32 - mmengine - INFO - Iter(train) [ 10900/120000] base_lr: 1.9600e-04 lr: 1.9636e-05 eta: 3 days, 13:17:41 time: 2.8158 data_time: 0.1927 memory: 14139 grad_norm: 0.6425 loss: 0.5949 detection_loss_cls: 0.5949 2024/07/10 13:22:55 - mmengine - INFO - Iter(train) [ 10950/120000] base_lr: 1.9596e-04 lr: 1.9633e-05 eta: 3 days, 13:15:39 time: 2.8166 data_time: 0.1928 memory: 14139 grad_norm: 0.6424 loss: 0.5947 detection_loss_cls: 0.5947 2024/07/10 13:25:16 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 13:25:16 - mmengine - INFO - Iter(train) [ 11000/120000] base_lr: 1.9592e-04 lr: 1.9629e-05 eta: 3 days, 13:13:24 time: 2.8168 data_time: 0.1928 memory: 14139 grad_norm: 0.6423 loss: 0.5944 detection_loss_cls: 0.5944 2024/07/10 13:25:16 - mmengine - INFO - Saving checkpoint at 11000 iterations 2024/07/10 13:27:45 - mmengine - INFO - Iter(train) [ 11050/120000] base_lr: 1.9589e-04 lr: 1.9626e-05 eta: 3 days, 13:12:30 time: 2.8173 data_time: 0.1932 memory: 14139 grad_norm: 0.6421 loss: 0.5939 detection_loss_cls: 0.5939 2024/07/10 13:30:07 - mmengine - INFO - Iter(train) [ 11100/120000] base_lr: 1.9585e-04 lr: 1.9623e-05 eta: 3 days, 13:10:17 time: 2.8176 data_time: 0.1934 memory: 14139 grad_norm: 0.6423 loss: 0.5940 detection_loss_cls: 0.5940 2024/07/10 13:32:26 - mmengine - INFO - Iter(train) [ 11150/120000] base_lr: 1.9581e-04 lr: 1.9619e-05 eta: 3 days, 13:07:40 time: 2.8172 data_time: 0.1934 memory: 14139 grad_norm: 0.6419 loss: 0.5934 detection_loss_cls: 0.5934 2024/07/10 13:34:47 - mmengine - INFO - Iter(train) [ 11200/120000] base_lr: 1.9578e-04 lr: 1.9616e-05 eta: 3 days, 13:05:24 time: 2.8173 data_time: 0.1933 memory: 14139 grad_norm: 0.6417 loss: 0.5930 detection_loss_cls: 0.5930 2024/07/10 13:37:07 - mmengine - INFO - Iter(train) [ 11250/120000] base_lr: 1.9574e-04 lr: 1.9613e-05 eta: 3 days, 13:02:55 time: 2.8175 data_time: 0.1931 memory: 14139 grad_norm: 0.6411 loss: 0.5917 detection_loss_cls: 0.5917 2024/07/10 13:39:28 - mmengine - INFO - Iter(train) [ 11300/120000] base_lr: 1.9570e-04 lr: 1.9609e-05 eta: 3 days, 13:00:36 time: 2.8177 data_time: 0.1931 memory: 14139 grad_norm: 0.6407 loss: 0.5914 detection_loss_cls: 0.5914 2024/07/10 13:41:50 - mmengine - INFO - Iter(train) [ 11350/120000] base_lr: 1.9566e-04 lr: 1.9606e-05 eta: 3 days, 12:58:25 time: 2.8184 data_time: 0.1930 memory: 14139 grad_norm: 0.6406 loss: 0.5904 detection_loss_cls: 0.5904 2024/07/10 13:44:10 - mmengine - INFO - Iter(train) [ 11400/120000] base_lr: 1.9562e-04 lr: 1.9602e-05 eta: 3 days, 12:55:55 time: 2.8181 data_time: 0.1931 memory: 14139 grad_norm: 0.6400 loss: 0.5898 detection_loss_cls: 0.5898 2024/07/10 13:46:31 - mmengine - INFO - Iter(train) [ 11450/120000] base_lr: 1.9559e-04 lr: 1.9599e-05 eta: 3 days, 12:53:40 time: 2.8180 data_time: 0.1939 memory: 14139 grad_norm: 0.6397 loss: 0.5902 detection_loss_cls: 0.5902 2024/07/10 13:48:52 - mmengine - INFO - Iter(train) [ 11500/120000] base_lr: 1.9555e-04 lr: 1.9595e-05 eta: 3 days, 12:51:16 time: 2.8180 data_time: 0.1943 memory: 14139 grad_norm: 0.6392 loss: 0.5901 detection_loss_cls: 0.5901 2024/07/10 13:51:11 - mmengine - INFO - Iter(train) [ 11550/120000] base_lr: 1.9551e-04 lr: 1.9592e-05 eta: 3 days, 12:48:43 time: 2.8179 data_time: 0.1947 memory: 14139 grad_norm: 0.6389 loss: 0.5901 detection_loss_cls: 0.5901 2024/07/10 13:53:33 - mmengine - INFO - Iter(train) [ 11600/120000] base_lr: 1.9547e-04 lr: 1.9588e-05 eta: 3 days, 12:46:31 time: 2.8182 data_time: 0.1953 memory: 14139 grad_norm: 0.6390 loss: 0.5902 detection_loss_cls: 0.5902 2024/07/10 13:55:54 - mmengine - INFO - Iter(train) [ 11650/120000] base_lr: 1.9543e-04 lr: 1.9585e-05 eta: 3 days, 12:44:12 time: 2.8183 data_time: 0.1952 memory: 14139 grad_norm: 0.6394 loss: 0.5892 detection_loss_cls: 0.5892 2024/07/10 13:58:14 - mmengine - INFO - Iter(train) [ 11700/120000] base_lr: 1.9539e-04 lr: 1.9581e-05 eta: 3 days, 12:41:49 time: 2.8183 data_time: 0.1956 memory: 14139 grad_norm: 0.6390 loss: 0.5894 detection_loss_cls: 0.5894 2024/07/10 14:00:35 - mmengine - INFO - Iter(train) [ 11750/120000] base_lr: 1.9535e-04 lr: 1.9578e-05 eta: 3 days, 12:39:28 time: 2.8180 data_time: 0.1954 memory: 14139 grad_norm: 0.6386 loss: 0.5886 detection_loss_cls: 0.5886 2024/07/10 14:02:56 - mmengine - INFO - Iter(train) [ 11800/120000] base_lr: 1.9531e-04 lr: 1.9574e-05 eta: 3 days, 12:37:09 time: 2.8180 data_time: 0.1953 memory: 14139 grad_norm: 0.6383 loss: 0.5882 detection_loss_cls: 0.5882 2024/07/10 14:05:16 - mmengine - INFO - Iter(train) [ 11850/120000] base_lr: 1.9527e-04 lr: 1.9570e-05 eta: 3 days, 12:34:42 time: 2.8182 data_time: 0.1949 memory: 14139 grad_norm: 0.6381 loss: 0.5876 detection_loss_cls: 0.5876 2024/07/10 14:07:37 - mmengine - INFO - Iter(train) [ 11900/120000] base_lr: 1.9524e-04 lr: 1.9567e-05 eta: 3 days, 12:32:19 time: 2.8185 data_time: 0.1948 memory: 14139 grad_norm: 0.6381 loss: 0.5868 detection_loss_cls: 0.5868 2024/07/10 14:09:57 - mmengine - INFO - Iter(train) [ 11950/120000] base_lr: 1.9520e-04 lr: 1.9563e-05 eta: 3 days, 12:29:52 time: 2.8189 data_time: 0.1947 memory: 14139 grad_norm: 0.6383 loss: 0.5863 detection_loss_cls: 0.5863 2024/07/10 14:12:17 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 14:12:17 - mmengine - INFO - Iter(train) [ 12000/120000] base_lr: 1.9516e-04 lr: 1.9560e-05 eta: 3 days, 12:27:29 time: 2.8192 data_time: 0.1947 memory: 14139 grad_norm: 0.6380 loss: 0.5855 detection_loss_cls: 0.5855 2024/07/10 14:12:17 - mmengine - INFO - Saving checkpoint at 12000 iterations 2024/07/10 14:14:47 - mmengine - INFO - Iter(train) [ 12050/120000] base_lr: 1.9512e-04 lr: 1.9556e-05 eta: 3 days, 12:26:25 time: 2.8198 data_time: 0.1946 memory: 14139 grad_norm: 0.6378 loss: 0.5848 detection_loss_cls: 0.5848 2024/07/10 14:17:07 - mmengine - INFO - Iter(train) [ 12100/120000] base_lr: 1.9507e-04 lr: 1.9552e-05 eta: 3 days, 12:23:56 time: 2.8199 data_time: 0.1948 memory: 14139 grad_norm: 0.6373 loss: 0.5848 detection_loss_cls: 0.5848 2024/07/10 14:19:27 - mmengine - INFO - Iter(train) [ 12150/120000] base_lr: 1.9503e-04 lr: 1.9549e-05 eta: 3 days, 12:21:32 time: 2.8200 data_time: 0.1954 memory: 14139 grad_norm: 0.6370 loss: 0.5852 detection_loss_cls: 0.5852 2024/07/10 14:21:48 - mmengine - INFO - Iter(train) [ 12200/120000] base_lr: 1.9499e-04 lr: 1.9545e-05 eta: 3 days, 12:19:15 time: 2.8203 data_time: 0.1958 memory: 14139 grad_norm: 0.6370 loss: 0.5852 detection_loss_cls: 0.5852 2024/07/10 14:24:08 - mmengine - INFO - Iter(train) [ 12250/120000] base_lr: 1.9495e-04 lr: 1.9541e-05 eta: 3 days, 12:16:44 time: 2.8201 data_time: 0.1957 memory: 14139 grad_norm: 0.6370 loss: 0.5838 detection_loss_cls: 0.5838 2024/07/10 14:26:28 - mmengine - INFO - Iter(train) [ 12300/120000] base_lr: 1.9491e-04 lr: 1.9537e-05 eta: 3 days, 12:14:16 time: 2.8200 data_time: 0.1960 memory: 14139 grad_norm: 0.6366 loss: 0.5836 detection_loss_cls: 0.5836 2024/07/10 14:28:48 - mmengine - INFO - Iter(train) [ 12350/120000] base_lr: 1.9487e-04 lr: 1.9534e-05 eta: 3 days, 12:11:51 time: 2.8201 data_time: 0.1964 memory: 14139 grad_norm: 0.6363 loss: 0.5835 detection_loss_cls: 0.5835 2024/07/10 14:31:10 - mmengine - INFO - Iter(train) [ 12400/120000] base_lr: 1.9483e-04 lr: 1.9530e-05 eta: 3 days, 12:09:34 time: 2.8207 data_time: 0.1966 memory: 14139 grad_norm: 0.6362 loss: 0.5828 detection_loss_cls: 0.5828 2024/07/10 14:33:30 - mmengine - INFO - Iter(train) [ 12450/120000] base_lr: 1.9479e-04 lr: 1.9526e-05 eta: 3 days, 12:07:08 time: 2.8208 data_time: 0.1970 memory: 14139 grad_norm: 0.6361 loss: 0.5826 detection_loss_cls: 0.5826 2024/07/10 14:35:51 - mmengine - INFO - Iter(train) [ 12500/120000] base_lr: 1.9475e-04 lr: 1.9522e-05 eta: 3 days, 12:04:51 time: 2.8210 data_time: 0.1971 memory: 14139 grad_norm: 0.6357 loss: 0.5819 detection_loss_cls: 0.5819 2024/07/10 14:38:11 - mmengine - INFO - Iter(train) [ 12550/120000] base_lr: 1.9471e-04 lr: 1.9519e-05 eta: 3 days, 12:02:28 time: 2.8212 data_time: 0.1979 memory: 14139 grad_norm: 0.6355 loss: 0.5824 detection_loss_cls: 0.5824 2024/07/10 14:40:31 - 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mmengine - INFO - Iter(train) [ 12850/120000] base_lr: 1.9445e-04 lr: 1.9496e-05 eta: 3 days, 11:47:55 time: 2.8212 data_time: 0.1993 memory: 14139 grad_norm: 0.6348 loss: 0.5808 detection_loss_cls: 0.5808 2024/07/10 14:54:34 - mmengine - INFO - Iter(train) [ 12900/120000] base_lr: 1.9441e-04 lr: 1.9492e-05 eta: 3 days, 11:45:36 time: 2.8214 data_time: 0.1995 memory: 14139 grad_norm: 0.6349 loss: 0.5802 detection_loss_cls: 0.5802 2024/07/10 14:56:54 - mmengine - INFO - Iter(train) [ 12950/120000] base_lr: 1.9437e-04 lr: 1.9488e-05 eta: 3 days, 11:43:10 time: 2.8217 data_time: 0.1994 memory: 14139 grad_norm: 0.6349 loss: 0.5796 detection_loss_cls: 0.5796 2024/07/10 14:59:16 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 14:59:16 - mmengine - INFO - Iter(train) [ 13000/120000] base_lr: 1.9432e-04 lr: 1.9484e-05 eta: 3 days, 11:40:56 time: 2.8221 data_time: 0.1996 memory: 14139 grad_norm: 0.6349 loss: 0.5796 detection_loss_cls: 0.5796 2024/07/10 14:59:16 - mmengine - INFO - Saving checkpoint at 13000 iterations 2024/07/10 15:01:46 - mmengine - INFO - Iter(train) [ 13050/120000] base_lr: 1.9428e-04 lr: 1.9480e-05 eta: 3 days, 11:39:54 time: 2.8225 data_time: 0.2001 memory: 14139 grad_norm: 0.6347 loss: 0.5799 detection_loss_cls: 0.5799 2024/07/10 15:04:08 - mmengine - INFO - Iter(train) [ 13100/120000] base_lr: 1.9424e-04 lr: 1.9476e-05 eta: 3 days, 11:37:44 time: 2.8229 data_time: 0.2004 memory: 14139 grad_norm: 0.6346 loss: 0.5802 detection_loss_cls: 0.5802 2024/07/10 15:06:29 - mmengine - INFO - Iter(train) [ 13150/120000] base_lr: 1.9419e-04 lr: 1.9472e-05 eta: 3 days, 11:35:20 time: 2.8229 data_time: 0.2007 memory: 14139 grad_norm: 0.6340 loss: 0.5807 detection_loss_cls: 0.5807 2024/07/10 15:08:50 - mmengine - INFO - Iter(train) [ 13200/120000] base_lr: 1.9415e-04 lr: 1.9468e-05 eta: 3 days, 11:33:06 time: 2.8233 data_time: 0.2010 memory: 14139 grad_norm: 0.6339 loss: 0.5804 detection_loss_cls: 0.5804 2024/07/10 15:11:12 - mmengine - INFO - Iter(train) [ 13250/120000] base_lr: 1.9410e-04 lr: 1.9464e-05 eta: 3 days, 11:30:52 time: 2.8237 data_time: 0.2011 memory: 14139 grad_norm: 0.6335 loss: 0.5799 detection_loss_cls: 0.5799 2024/07/10 15:13:32 - mmengine - INFO - Iter(train) [ 13300/120000] base_lr: 1.9406e-04 lr: 1.9460e-05 eta: 3 days, 11:28:29 time: 2.8238 data_time: 0.2014 memory: 14139 grad_norm: 0.6336 loss: 0.5797 detection_loss_cls: 0.5797 2024/07/10 15:15:53 - mmengine - INFO - Iter(train) [ 13350/120000] base_lr: 1.9402e-04 lr: 1.9456e-05 eta: 3 days, 11:26:10 time: 2.8242 data_time: 0.2015 memory: 14139 grad_norm: 0.6336 loss: 0.5792 detection_loss_cls: 0.5792 2024/07/10 15:18:15 - mmengine - INFO - Iter(train) [ 13400/120000] base_lr: 1.9397e-04 lr: 1.9452e-05 eta: 3 days, 11:23:52 time: 2.8246 data_time: 0.2018 memory: 14139 grad_norm: 0.6334 loss: 0.5791 detection_loss_cls: 0.5791 2024/07/10 15:20:36 - mmengine - INFO - Iter(train) [ 13450/120000] base_lr: 1.9393e-04 lr: 1.9448e-05 eta: 3 days, 11:21:37 time: 2.8250 data_time: 0.2017 memory: 14139 grad_norm: 0.6334 loss: 0.5778 detection_loss_cls: 0.5778 2024/07/10 15:22:58 - mmengine - INFO - Iter(train) [ 13500/120000] base_lr: 1.9388e-04 lr: 1.9444e-05 eta: 3 days, 11:19:22 time: 2.8252 data_time: 0.2020 memory: 14139 grad_norm: 0.6331 loss: 0.5778 detection_loss_cls: 0.5778 2024/07/10 15:25:20 - mmengine - INFO - Iter(train) [ 13550/120000] base_lr: 1.9384e-04 lr: 1.9440e-05 eta: 3 days, 11:17:08 time: 2.8254 data_time: 0.2021 memory: 14139 grad_norm: 0.6331 loss: 0.5773 detection_loss_cls: 0.5773 2024/07/10 15:27:40 - mmengine - INFO - Iter(train) [ 13600/120000] base_lr: 1.9379e-04 lr: 1.9436e-05 eta: 3 days, 11:14:43 time: 2.8255 data_time: 0.2022 memory: 14139 grad_norm: 0.6327 loss: 0.5771 detection_loss_cls: 0.5771 2024/07/10 15:30:00 - mmengine - INFO - Iter(train) [ 13650/120000] base_lr: 1.9375e-04 lr: 1.9432e-05 eta: 3 days, 11:12:18 time: 2.8251 data_time: 0.2024 memory: 14139 grad_norm: 0.6326 loss: 0.5768 detection_loss_cls: 0.5768 2024/07/10 15:32:22 - mmengine - INFO - Iter(train) [ 13700/120000] base_lr: 1.9370e-04 lr: 1.9427e-05 eta: 3 days, 11:10:03 time: 2.8253 data_time: 0.2025 memory: 14139 grad_norm: 0.6325 loss: 0.5762 detection_loss_cls: 0.5762 2024/07/10 15:34:41 - mmengine - INFO - Iter(train) [ 13750/120000] base_lr: 1.9366e-04 lr: 1.9423e-05 eta: 3 days, 11:07:34 time: 2.8255 data_time: 0.2028 memory: 14139 grad_norm: 0.6323 loss: 0.5763 detection_loss_cls: 0.5763 2024/07/10 15:37:03 - mmengine - INFO - Iter(train) [ 13800/120000] base_lr: 1.9361e-04 lr: 1.9419e-05 eta: 3 days, 11:05:18 time: 2.8260 data_time: 0.2032 memory: 14139 grad_norm: 0.6321 loss: 0.5756 detection_loss_cls: 0.5756 2024/07/10 15:39:25 - mmengine - INFO - Iter(train) [ 13850/120000] base_lr: 1.9356e-04 lr: 1.9415e-05 eta: 3 days, 11:03:05 time: 2.8264 data_time: 0.2034 memory: 14139 grad_norm: 0.6318 loss: 0.5750 detection_loss_cls: 0.5750 2024/07/10 15:41:45 - mmengine - INFO - Iter(train) [ 13900/120000] base_lr: 1.9352e-04 lr: 1.9411e-05 eta: 3 days, 11:00:39 time: 2.8266 data_time: 0.2037 memory: 14139 grad_norm: 0.6319 loss: 0.5747 detection_loss_cls: 0.5747 2024/07/10 15:44:05 - mmengine - INFO - Iter(train) [ 13950/120000] base_lr: 1.9347e-04 lr: 1.9407e-05 eta: 3 days, 10:58:15 time: 2.8266 data_time: 0.2035 memory: 14139 grad_norm: 0.6318 loss: 0.5734 detection_loss_cls: 0.5734 2024/07/10 15:46:27 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 15:46:27 - mmengine - INFO - Iter(train) [ 14000/120000] base_lr: 1.9343e-04 lr: 1.9402e-05 eta: 3 days, 10:55:58 time: 2.8271 data_time: 0.2038 memory: 14139 grad_norm: 0.6316 loss: 0.5729 detection_loss_cls: 0.5729 2024/07/10 15:46:27 - mmengine - INFO - Saving checkpoint at 14000 iterations 2024/07/10 15:48:57 - mmengine - INFO - Iter(train) [ 14050/120000] base_lr: 1.9338e-04 lr: 1.9398e-05 eta: 3 days, 10:54:45 time: 2.8221 data_time: 0.1985 memory: 14139 grad_norm: 0.6316 loss: 0.5725 detection_loss_cls: 0.5725 2024/07/10 15:51:18 - mmengine - INFO - Iter(train) [ 14100/120000] base_lr: 1.9333e-04 lr: 1.9394e-05 eta: 3 days, 10:52:27 time: 2.8222 data_time: 0.1988 memory: 14139 grad_norm: 0.6315 loss: 0.5724 detection_loss_cls: 0.5724 2024/07/10 15:53:40 - mmengine - INFO - Iter(train) [ 14150/120000] base_lr: 1.9329e-04 lr: 1.9390e-05 eta: 3 days, 10:50:14 time: 2.8226 data_time: 0.1988 memory: 14139 grad_norm: 0.6312 loss: 0.5719 detection_loss_cls: 0.5719 2024/07/10 15:56:01 - mmengine - INFO - Iter(train) [ 14200/120000] base_lr: 1.9324e-04 lr: 1.9385e-05 eta: 3 days, 10:47:56 time: 2.8229 data_time: 0.1989 memory: 14139 grad_norm: 0.6310 loss: 0.5721 detection_loss_cls: 0.5721 2024/07/10 15:58:22 - mmengine - INFO - Iter(train) [ 14250/120000] base_lr: 1.9319e-04 lr: 1.9381e-05 eta: 3 days, 10:45:37 time: 2.8230 data_time: 0.1989 memory: 14139 grad_norm: 0.6312 loss: 0.5720 detection_loss_cls: 0.5720 2024/07/10 16:00:45 - mmengine - INFO - Iter(train) [ 14300/120000] base_lr: 1.9314e-04 lr: 1.9377e-05 eta: 3 days, 10:43:34 time: 2.8237 data_time: 0.1990 memory: 14139 grad_norm: 0.6314 loss: 0.5718 detection_loss_cls: 0.5718 2024/07/10 16:03:07 - mmengine - INFO - Iter(train) [ 14350/120000] base_lr: 1.9310e-04 lr: 1.9372e-05 eta: 3 days, 10:41:21 time: 2.8244 data_time: 0.1992 memory: 14139 grad_norm: 0.6315 loss: 0.5722 detection_loss_cls: 0.5722 2024/07/10 16:05:29 - mmengine - INFO - Iter(train) [ 14400/120000] base_lr: 1.9305e-04 lr: 1.9368e-05 eta: 3 days, 10:39:02 time: 2.8245 data_time: 0.1991 memory: 14139 grad_norm: 0.6312 loss: 0.5723 detection_loss_cls: 0.5723 2024/07/10 16:07:49 - mmengine - INFO - Iter(train) [ 14450/120000] base_lr: 1.9300e-04 lr: 1.9364e-05 eta: 3 days, 10:36:41 time: 2.8246 data_time: 0.1992 memory: 14139 grad_norm: 0.6314 loss: 0.5723 detection_loss_cls: 0.5723 2024/07/10 16:10:11 - mmengine - INFO - Iter(train) [ 14500/120000] base_lr: 1.9295e-04 lr: 1.9359e-05 eta: 3 days, 10:34:23 time: 2.8249 data_time: 0.1997 memory: 14139 grad_norm: 0.6312 loss: 0.5727 detection_loss_cls: 0.5727 2024/07/10 16:12:32 - mmengine - INFO - Iter(train) [ 14550/120000] base_lr: 1.9291e-04 lr: 1.9355e-05 eta: 3 days, 10:32:09 time: 2.8255 data_time: 0.1998 memory: 14139 grad_norm: 0.6308 loss: 0.5727 detection_loss_cls: 0.5727 2024/07/10 16:14:53 - mmengine - INFO - Iter(train) [ 14600/120000] base_lr: 1.9286e-04 lr: 1.9351e-05 eta: 3 days, 10:29:46 time: 2.8260 data_time: 0.1999 memory: 14139 grad_norm: 0.6306 loss: 0.5727 detection_loss_cls: 0.5727 2024/07/10 16:17:15 - mmengine - INFO - Iter(train) [ 14650/120000] base_lr: 1.9281e-04 lr: 1.9346e-05 eta: 3 days, 10:27:32 time: 2.8262 data_time: 0.2000 memory: 14139 grad_norm: 0.6307 loss: 0.5725 detection_loss_cls: 0.5725 2024/07/10 16:19:36 - mmengine - INFO - Iter(train) [ 14700/120000] base_lr: 1.9276e-04 lr: 1.9342e-05 eta: 3 days, 10:25:14 time: 2.8266 data_time: 0.2002 memory: 14139 grad_norm: 0.6303 loss: 0.5722 detection_loss_cls: 0.5722 2024/07/10 16:21:59 - mmengine - INFO - Iter(train) [ 14750/120000] base_lr: 1.9271e-04 lr: 1.9337e-05 eta: 3 days, 10:23:06 time: 2.8268 data_time: 0.2004 memory: 14139 grad_norm: 0.6303 loss: 0.5722 detection_loss_cls: 0.5722 2024/07/10 16:24:20 - mmengine - INFO - Iter(train) [ 14800/120000] base_lr: 1.9266e-04 lr: 1.9333e-05 eta: 3 days, 10:20:47 time: 2.8269 data_time: 0.2006 memory: 14139 grad_norm: 0.6299 loss: 0.5724 detection_loss_cls: 0.5724 2024/07/10 16:26:41 - mmengine - INFO - Iter(train) [ 14850/120000] base_lr: 1.9261e-04 lr: 1.9328e-05 eta: 3 days, 10:18:27 time: 2.8273 data_time: 0.2005 memory: 14139 grad_norm: 0.6298 loss: 0.5720 detection_loss_cls: 0.5720 2024/07/10 16:29:04 - mmengine - INFO - Iter(train) [ 14900/120000] base_lr: 1.9256e-04 lr: 1.9324e-05 eta: 3 days, 10:16:20 time: 2.8278 data_time: 0.2006 memory: 14139 grad_norm: 0.6298 loss: 0.5725 detection_loss_cls: 0.5725 2024/07/10 16:31:25 - mmengine - INFO - Iter(train) [ 14950/120000] base_lr: 1.9251e-04 lr: 1.9320e-05 eta: 3 days, 10:14:00 time: 2.8275 data_time: 0.2009 memory: 14139 grad_norm: 0.6295 loss: 0.5726 detection_loss_cls: 0.5726 2024/07/10 16:33:46 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 16:33:46 - mmengine - INFO - Iter(train) [ 15000/120000] base_lr: 1.9247e-04 lr: 1.9315e-05 eta: 3 days, 10:11:38 time: 2.8273 data_time: 0.2011 memory: 14139 grad_norm: 0.6292 loss: 0.5723 detection_loss_cls: 0.5723 2024/07/10 16:33:46 - mmengine - INFO - Saving checkpoint at 15000 iterations 2024/07/10 16:34:16 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:35 time: 0.4047 data_time: 0.0048 memory: 5706 2024/07/10 16:34:36 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:10 time: 0.4051 data_time: 0.0048 memory: 5706 2024/07/10 16:34:58 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:49 time: 0.4055 data_time: 0.0048 memory: 5706 2024/07/10 16:35:19 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:27 time: 0.4060 data_time: 0.0048 memory: 5706 2024/07/10 16:35:40 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:06 time: 0.4064 data_time: 0.0048 memory: 5706 2024/07/10 16:36:01 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:45 time: 0.4068 data_time: 0.0048 memory: 5706 2024/07/10 16:36:22 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:24 time: 0.4071 data_time: 0.0048 memory: 5706 2024/07/10 16:36:43 - mmengine - INFO - Iter(val) [400/834] eta: 0:03:03 time: 0.4075 data_time: 0.0048 memory: 5706 2024/07/10 16:37:04 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:41 time: 0.4078 data_time: 0.0048 memory: 5706 2024/07/10 16:37:25 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:20 time: 0.4081 data_time: 0.0048 memory: 5706 2024/07/10 16:37:46 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:59 time: 0.4083 data_time: 0.0048 memory: 5706 2024/07/10 16:38:07 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:38 time: 0.4087 data_time: 0.0048 memory: 5706 2024/07/10 16:38:28 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:17 time: 0.4089 data_time: 0.0048 memory: 5706 2024/07/10 16:38:50 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:56 time: 0.4093 data_time: 0.0048 memory: 5706 2024/07/10 16:39:10 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:35 time: 0.4094 data_time: 0.0048 memory: 5706 2024/07/10 16:39:32 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:14 time: 0.4097 data_time: 0.0047 memory: 5706 2024/07/10 16:39:48 - mmengine - INFO - Evaluating bbox... 2024/07/10 16:40:17 - mmengine - INFO - bbox_mAP_copypaste: 0.373 0.530 0.408 0.200 0.405 0.498 2024/07/10 16:40:17 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.3730 coco/bbox_mAP_50: 0.5300 coco/bbox_mAP_75: 0.4080 coco/bbox_mAP_s: 0.2000 coco/bbox_mAP_m: 0.4050 coco/bbox_mAP_l: 0.4980 data_time: 0.0047 time: 0.4216 2024/07/10 16:42:38 - mmengine - INFO - Iter(train) [ 15050/120000] base_lr: 1.9242e-04 lr: 1.9310e-05 eta: 3 days, 10:12:54 time: 2.8329 data_time: 0.2067 memory: 14139 grad_norm: 0.6290 loss: 0.5721 detection_loss_cls: 0.5721 2024/07/10 16:44:59 - mmengine - INFO - Iter(train) [ 15100/120000] base_lr: 1.9237e-04 lr: 1.9306e-05 eta: 3 days, 10:10:35 time: 2.8329 data_time: 0.2069 memory: 14139 grad_norm: 0.6284 loss: 0.5717 detection_loss_cls: 0.5717 2024/07/10 16:47:19 - mmengine - INFO - Iter(train) [ 15150/120000] base_lr: 1.9232e-04 lr: 1.9301e-05 eta: 3 days, 10:08:07 time: 2.8331 data_time: 0.2073 memory: 14139 grad_norm: 0.6281 loss: 0.5721 detection_loss_cls: 0.5721 2024/07/10 16:49:41 - mmengine - INFO - Iter(train) [ 15200/120000] base_lr: 1.9227e-04 lr: 1.9297e-05 eta: 3 days, 10:05:49 time: 2.8331 data_time: 0.2071 memory: 14139 grad_norm: 0.6279 loss: 0.5712 detection_loss_cls: 0.5712 2024/07/10 16:52:03 - mmengine - INFO - Iter(train) [ 15250/120000] base_lr: 1.9222e-04 lr: 1.9292e-05 eta: 3 days, 10:03:36 time: 2.8337 data_time: 0.2074 memory: 14139 grad_norm: 0.6277 loss: 0.5714 detection_loss_cls: 0.5714 2024/07/10 16:54:24 - mmengine - INFO - Iter(train) [ 15300/120000] base_lr: 1.9216e-04 lr: 1.9288e-05 eta: 3 days, 10:01:16 time: 2.8337 data_time: 0.2071 memory: 14139 grad_norm: 0.6276 loss: 0.5706 detection_loss_cls: 0.5706 2024/07/10 16:56:45 - 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mmengine - INFO - Saving checkpoint at 16000 iterations 2024/07/10 17:29:43 - mmengine - INFO - Iter(train) [ 16050/120000] base_lr: 1.9139e-04 lr: 1.9217e-05 eta: 3 days, 9:26:26 time: 2.8339 data_time: 0.2087 memory: 14139 grad_norm: 0.6258 loss: 0.5688 detection_loss_cls: 0.5688 2024/07/10 17:32:03 - mmengine - INFO - Iter(train) [ 16100/120000] base_lr: 1.9134e-04 lr: 1.9212e-05 eta: 3 days, 9:23:55 time: 2.8338 data_time: 0.2085 memory: 14139 grad_norm: 0.6257 loss: 0.5677 detection_loss_cls: 0.5677 2024/07/10 17:34:24 - mmengine - INFO - Iter(train) [ 16150/120000] base_lr: 1.9128e-04 lr: 1.9208e-05 eta: 3 days, 9:21:37 time: 2.8341 data_time: 0.2086 memory: 14139 grad_norm: 0.6255 loss: 0.5673 detection_loss_cls: 0.5673 2024/07/10 17:36:44 - mmengine - INFO - Iter(train) [ 16200/120000] base_lr: 1.9123e-04 lr: 1.9203e-05 eta: 3 days, 9:19:06 time: 2.8337 data_time: 0.2091 memory: 14139 grad_norm: 0.6256 loss: 0.5682 detection_loss_cls: 0.5682 2024/07/10 17:39:04 - mmengine - INFO - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 18:14:15 - mmengine - INFO - Iter(train) [ 17000/120000] base_lr: 1.9036e-04 lr: 1.9123e-05 eta: 3 days, 8:40:59 time: 2.8347 data_time: 0.2104 memory: 14139 grad_norm: 0.6272 loss: 0.5674 detection_loss_cls: 0.5674 2024/07/10 18:14:15 - mmengine - INFO - Saving checkpoint at 17000 iterations 2024/07/10 18:16:43 - mmengine - INFO - Iter(train) [ 17050/120000] base_lr: 1.9030e-04 lr: 1.9118e-05 eta: 3 days, 8:39:23 time: 2.8342 data_time: 0.2105 memory: 14139 grad_norm: 0.6271 loss: 0.5670 detection_loss_cls: 0.5670 2024/07/10 18:19:03 - mmengine - INFO - Iter(train) [ 17100/120000] base_lr: 1.9025e-04 lr: 1.9113e-05 eta: 3 days, 8:36:54 time: 2.8336 data_time: 0.2100 memory: 14139 grad_norm: 0.6268 loss: 0.5659 detection_loss_cls: 0.5659 2024/07/10 18:21:23 - mmengine - INFO - Iter(train) [ 17150/120000] base_lr: 1.9019e-04 lr: 1.9108e-05 eta: 3 days, 8:34:29 time: 2.8335 data_time: 0.2100 memory: 14139 grad_norm: 0.6267 loss: 0.5651 detection_loss_cls: 0.5651 2024/07/10 18:23:44 - 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mmengine - INFO - Iter(train) [ 17950/120000] base_lr: 1.8927e-04 lr: 1.9025e-05 eta: 3 days, 7:56:11 time: 2.8316 data_time: 0.2101 memory: 14139 grad_norm: 0.6253 loss: 0.5631 detection_loss_cls: 0.5631 2024/07/10 19:01:12 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 19:01:12 - mmengine - INFO - Iter(train) [ 18000/120000] base_lr: 1.8921e-04 lr: 1.9019e-05 eta: 3 days, 7:53:43 time: 2.8312 data_time: 0.2099 memory: 14139 grad_norm: 0.6251 loss: 0.5630 detection_loss_cls: 0.5630 2024/07/10 19:01:12 - mmengine - INFO - Saving checkpoint at 18000 iterations 2024/07/10 19:03:42 - mmengine - INFO - Iter(train) [ 18050/120000] base_lr: 1.8915e-04 lr: 1.9014e-05 eta: 3 days, 7:52:12 time: 2.8312 data_time: 0.2100 memory: 14139 grad_norm: 0.6247 loss: 0.5632 detection_loss_cls: 0.5632 2024/07/10 19:06:03 - mmengine - INFO - Iter(train) [ 18100/120000] base_lr: 1.8909e-04 lr: 1.9008e-05 eta: 3 days, 7:49:50 time: 2.8311 data_time: 0.2100 memory: 14139 grad_norm: 0.6251 loss: 0.5633 detection_loss_cls: 0.5633 2024/07/10 19:08:25 - 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mmengine - INFO - Iter(train) [ 18900/120000] base_lr: 1.8813e-04 lr: 1.8921e-05 eta: 3 days, 7:12:08 time: 2.8284 data_time: 0.2106 memory: 14139 grad_norm: 0.6230 loss: 0.5607 detection_loss_cls: 0.5607 2024/07/10 19:45:58 - mmengine - INFO - Iter(train) [ 18950/120000] base_lr: 1.8807e-04 lr: 1.8915e-05 eta: 3 days, 7:09:40 time: 2.8281 data_time: 0.2105 memory: 14139 grad_norm: 0.6231 loss: 0.5604 detection_loss_cls: 0.5604 2024/07/10 19:48:19 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 19:48:19 - mmengine - INFO - Iter(train) [ 19000/120000] base_lr: 1.8800e-04 lr: 1.8909e-05 eta: 3 days, 7:07:20 time: 2.8281 data_time: 0.2105 memory: 14139 grad_norm: 0.6233 loss: 0.5601 detection_loss_cls: 0.5601 2024/07/10 19:48:19 - mmengine - INFO - Saving checkpoint at 19000 iterations 2024/07/10 19:50:48 - mmengine - INFO - Iter(train) [ 19050/120000] base_lr: 1.8794e-04 lr: 1.8904e-05 eta: 3 days, 7:05:39 time: 2.8223 data_time: 0.2049 memory: 14139 grad_norm: 0.6231 loss: 0.5606 detection_loss_cls: 0.5606 2024/07/10 19:53:08 - mmengine - INFO - Iter(train) [ 19100/120000] base_lr: 1.8788e-04 lr: 1.8898e-05 eta: 3 days, 7:03:16 time: 2.8221 data_time: 0.2051 memory: 14139 grad_norm: 0.6233 loss: 0.5610 detection_loss_cls: 0.5610 2024/07/10 19:55:28 - mmengine - INFO - Iter(train) [ 19150/120000] base_lr: 1.8782e-04 lr: 1.8893e-05 eta: 3 days, 7:00:49 time: 2.8221 data_time: 0.2050 memory: 14139 grad_norm: 0.6237 loss: 0.5607 detection_loss_cls: 0.5607 2024/07/10 19:57:50 - mmengine - INFO - Iter(train) [ 19200/120000] base_lr: 1.8776e-04 lr: 1.8887e-05 eta: 3 days, 6:58:30 time: 2.8221 data_time: 0.2053 memory: 14139 grad_norm: 0.6239 loss: 0.5612 detection_loss_cls: 0.5612 2024/07/10 20:00:10 - mmengine - INFO - Iter(train) [ 19250/120000] base_lr: 1.8769e-04 lr: 1.8881e-05 eta: 3 days, 6:56:04 time: 2.8216 data_time: 0.2051 memory: 14139 grad_norm: 0.6237 loss: 0.5612 detection_loss_cls: 0.5612 2024/07/10 20:02:30 - mmengine - INFO - Iter(train) [ 19300/120000] base_lr: 1.8763e-04 lr: 1.8875e-05 eta: 3 days, 6:53:38 time: 2.8213 data_time: 0.2050 memory: 14139 grad_norm: 0.6237 loss: 0.5609 detection_loss_cls: 0.5609 2024/07/10 20:04:52 - mmengine - INFO - Iter(train) [ 19350/120000] base_lr: 1.8757e-04 lr: 1.8870e-05 eta: 3 days, 6:51:22 time: 2.8214 data_time: 0.2050 memory: 14139 grad_norm: 0.6235 loss: 0.5609 detection_loss_cls: 0.5609 2024/07/10 20:07:13 - mmengine - INFO - Iter(train) [ 19400/120000] base_lr: 1.8750e-04 lr: 1.8864e-05 eta: 3 days, 6:49:02 time: 2.8214 data_time: 0.2051 memory: 14139 grad_norm: 0.6230 loss: 0.5610 detection_loss_cls: 0.5610 2024/07/10 20:09:33 - mmengine - INFO - Iter(train) [ 19450/120000] base_lr: 1.8744e-04 lr: 1.8858e-05 eta: 3 days, 6:46:34 time: 2.8212 data_time: 0.2053 memory: 14139 grad_norm: 0.6228 loss: 0.5616 detection_loss_cls: 0.5616 2024/07/10 20:11:54 - mmengine - INFO - Iter(train) [ 19500/120000] base_lr: 1.8738e-04 lr: 1.8853e-05 eta: 3 days, 6:44:12 time: 2.8212 data_time: 0.2051 memory: 14139 grad_norm: 0.6226 loss: 0.5612 detection_loss_cls: 0.5612 2024/07/10 20:14:15 - mmengine - INFO - Iter(train) [ 19550/120000] base_lr: 1.8731e-04 lr: 1.8847e-05 eta: 3 days, 6:41:52 time: 2.8214 data_time: 0.2050 memory: 14139 grad_norm: 0.6225 loss: 0.5610 detection_loss_cls: 0.5610 2024/07/10 20:16:35 - mmengine - INFO - Iter(train) [ 19600/120000] base_lr: 1.8725e-04 lr: 1.8841e-05 eta: 3 days, 6:39:27 time: 2.8213 data_time: 0.2048 memory: 14139 grad_norm: 0.6222 loss: 0.5601 detection_loss_cls: 0.5601 2024/07/10 20:18:56 - mmengine - INFO - Iter(train) [ 19650/120000] base_lr: 1.8719e-04 lr: 1.8835e-05 eta: 3 days, 6:37:03 time: 2.8213 data_time: 0.2046 memory: 14139 grad_norm: 0.6221 loss: 0.5598 detection_loss_cls: 0.5598 2024/07/10 20:21:17 - mmengine - INFO - Iter(train) [ 19700/120000] base_lr: 1.8712e-04 lr: 1.8829e-05 eta: 3 days, 6:34:44 time: 2.8215 data_time: 0.2047 memory: 14139 grad_norm: 0.6224 loss: 0.5592 detection_loss_cls: 0.5592 2024/07/10 20:23:39 - mmengine - INFO - Iter(train) [ 19750/120000] base_lr: 1.8706e-04 lr: 1.8824e-05 eta: 3 days, 6:32:26 time: 2.8216 data_time: 0.2047 memory: 14139 grad_norm: 0.6220 loss: 0.5590 detection_loss_cls: 0.5590 2024/07/10 20:26:00 - mmengine - INFO - Iter(train) [ 19800/120000] base_lr: 1.8700e-04 lr: 1.8818e-05 eta: 3 days, 6:30:08 time: 2.8218 data_time: 0.2043 memory: 14139 grad_norm: 0.6219 loss: 0.5578 detection_loss_cls: 0.5578 2024/07/10 20:28:21 - mmengine - INFO - Iter(train) [ 19850/120000] base_lr: 1.8693e-04 lr: 1.8812e-05 eta: 3 days, 6:27:46 time: 2.8219 data_time: 0.2040 memory: 14139 grad_norm: 0.6221 loss: 0.5573 detection_loss_cls: 0.5573 2024/07/10 20:30:43 - mmengine - INFO - Iter(train) [ 19900/120000] base_lr: 1.8687e-04 lr: 1.8806e-05 eta: 3 days, 6:25:29 time: 2.8224 data_time: 0.2045 memory: 14139 grad_norm: 0.6220 loss: 0.5582 detection_loss_cls: 0.5582 2024/07/10 20:33:04 - mmengine - INFO - Iter(train) [ 19950/120000] base_lr: 1.8680e-04 lr: 1.8800e-05 eta: 3 days, 6:23:08 time: 2.8226 data_time: 0.2046 memory: 14139 grad_norm: 0.6218 loss: 0.5584 detection_loss_cls: 0.5584 2024/07/10 20:35:25 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240710_043507 2024/07/10 20:35:25 - mmengine - INFO - Iter(train) [ 20000/120000] base_lr: 1.8674e-04 lr: 1.8794e-05 eta: 3 days, 6:20:48 time: 2.8226 data_time: 0.2049 memory: 14139 grad_norm: 0.6217 loss: 0.5584 detection_loss_cls: 0.5584 2024/07/10 20:35:25 - mmengine - INFO - Saving checkpoint at 20000 iterations 2024/07/10 20:35:55 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:24 time: 0.4099 data_time: 0.0048 memory: 5706 2024/07/10 20:36:15 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:01 time: 0.4099 data_time: 0.0048 memory: 5706 2024/07/10 20:36:35 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:40 time: 0.4099 data_time: 0.0048 memory: 5706 2024/07/10 20:36:56 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:20 time: 0.4099 data_time: 0.0048 memory: 5706 2024/07/10 20:37:17 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:00 time: 0.4100 data_time: 0.0048 memory: 5706 2024/07/10 20:37:37 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:39 time: 0.4099 data_time: 0.0047 memory: 5706 2024/07/10 20:37:58 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:18 time: 0.4099 data_time: 0.0047 memory: 5706 2024/07/10 20:38:18 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:58 time: 0.4100 data_time: 0.0047 memory: 5706 2024/07/10 20:38:38 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:37 time: 0.4099 data_time: 0.0047 memory: 5706 2024/07/10 20:38:59 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:17 time: 0.4099 data_time: 0.0047 memory: 5706 2024/07/10 20:39:19 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4098 data_time: 0.0047 memory: 5706 2024/07/10 20:39:40 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:35 time: 0.4099 data_time: 0.0047 memory: 5706 2024/07/10 20:40:00 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4098 data_time: 0.0047 memory: 5706 2024/07/10 20:40:21 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4099 data_time: 0.0047 memory: 5706 2024/07/10 20:40:41 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4099 data_time: 0.0047 memory: 5706 2024/07/10 20:41:02 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4099 data_time: 0.0047 memory: 5706 2024/07/10 20:41:18 - mmengine - INFO - Evaluating bbox... 2024/07/10 20:41:46 - mmengine - INFO - bbox_mAP_copypaste: 0.391 0.549 0.427 0.214 0.423 0.522 2024/07/10 20:41:46 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.3910 coco/bbox_mAP_50: 0.5490 coco/bbox_mAP_75: 0.4270 coco/bbox_mAP_s: 0.2140 coco/bbox_mAP_m: 0.4230 coco/bbox_mAP_l: 0.5220 data_time: 0.0047 time: 0.4098 2024/07/11 11:04:18 - mmengine - INFO - Iter(train) [ 20050/120000] base_lr: 1.8667e-04 lr: 1.8788e-05 eta: 3 days, 7:12:53 time: 2.8210 data_time: 0.2020 memory: 14080 grad_norm: 0.6215 loss: 0.5582 detection_loss_cls: 0.5582 2024/07/11 11:06:40 - mmengine - INFO - Iter(train) [ 20100/120000] base_lr: 1.8661e-04 lr: 1.8783e-05 eta: 3 days, 6:55:31 time: 2.8216 data_time: 0.2017 memory: 14080 grad_norm: 0.6215 loss: 0.5584 detection_loss_cls: 0.5584 2024/07/11 11:09:02 - mmengine - INFO - Iter(train) [ 20150/120000] base_lr: 1.8654e-04 lr: 1.8777e-05 eta: 3 days, 6:54:14 time: 2.8218 data_time: 0.2011 memory: 14080 grad_norm: 0.6213 loss: 0.5580 detection_loss_cls: 0.5580 2024/07/11 11:11:25 - mmengine - INFO - Iter(train) [ 20200/120000] base_lr: 1.8648e-04 lr: 1.8771e-05 eta: 3 days, 6:51:15 time: 2.8225 data_time: 0.2006 memory: 14080 grad_norm: 0.6211 loss: 0.5579 detection_loss_cls: 0.5579 2024/07/11 11:13:45 - mmengine - INFO - Iter(train) [ 20250/120000] base_lr: 1.8641e-04 lr: 1.8765e-05 eta: 3 days, 6:36:48 time: 2.8226 data_time: 0.1999 memory: 14080 grad_norm: 0.6212 loss: 0.5572 detection_loss_cls: 0.5572 2024/07/11 11:16:05 - mmengine - INFO - Iter(train) [ 20300/120000] base_lr: 1.8635e-04 lr: 1.8759e-05 eta: 3 days, 6:26:20 time: 2.8225 data_time: 0.1993 memory: 14080 grad_norm: 0.6214 loss: 0.5564 detection_loss_cls: 0.5564 2024/07/11 11:18:25 - mmengine - INFO - Iter(train) [ 20350/120000] base_lr: 1.8628e-04 lr: 1.8753e-05 eta: 3 days, 6:16:04 time: 2.8223 data_time: 0.1989 memory: 14080 grad_norm: 0.6211 loss: 0.5564 detection_loss_cls: 0.5564 2024/07/11 11:20:45 - mmengine - INFO - Iter(train) [ 20400/120000] base_lr: 1.8621e-04 lr: 1.8747e-05 eta: 3 days, 6:05:11 time: 2.8219 data_time: 0.1984 memory: 14080 grad_norm: 0.6210 loss: 0.5565 detection_loss_cls: 0.5565 2024/07/11 11:23:05 - mmengine - INFO - Iter(train) [ 20450/120000] base_lr: 1.8615e-04 lr: 1.8741e-05 eta: 3 days, 5:59:19 time: 2.8220 data_time: 0.1978 memory: 14080 grad_norm: 0.6210 loss: 0.5562 detection_loss_cls: 0.5562 2024/07/11 11:25:27 - mmengine - INFO - Iter(train) [ 20500/120000] base_lr: 1.8608e-04 lr: 1.8735e-05 eta: 3 days, 6:00:39 time: 2.8224 data_time: 0.1974 memory: 14080 grad_norm: 0.6209 loss: 0.5566 detection_loss_cls: 0.5566 2024/07/11 11:27:48 - mmengine - INFO - Iter(train) [ 20550/120000] base_lr: 1.8602e-04 lr: 1.8729e-05 eta: 3 days, 5:58:32 time: 2.8228 data_time: 0.1970 memory: 14080 grad_norm: 0.6193 loss: 0.5566 detection_loss_cls: 0.5566 2024/07/11 11:30:10 - mmengine - INFO - Iter(train) [ 20600/120000] base_lr: 1.8595e-04 lr: 1.8723e-05 eta: 3 days, 5:57:20 time: 2.8230 data_time: 0.1963 memory: 14080 grad_norm: 0.6191 loss: 0.5564 detection_loss_cls: 0.5564 2024/07/11 11:32:30 - mmengine - INFO - Iter(train) [ 20650/120000] base_lr: 1.8588e-04 lr: 1.8717e-05 eta: 3 days, 5:51:53 time: 2.8225 data_time: 0.1956 memory: 14080 grad_norm: 0.6194 loss: 0.5559 detection_loss_cls: 0.5559 2024/07/11 11:34:50 - mmengine - INFO - Iter(train) [ 20700/120000] base_lr: 1.8582e-04 lr: 1.8711e-05 eta: 3 days, 5:48:24 time: 2.8227 data_time: 0.1950 memory: 14080 grad_norm: 0.6191 loss: 0.5555 detection_loss_cls: 0.5555 2024/07/11 11:37:10 - mmengine - INFO - Iter(train) [ 20750/120000] base_lr: 1.8575e-04 lr: 1.8704e-05 eta: 3 days, 5:43:42 time: 2.8222 data_time: 0.1945 memory: 14080 grad_norm: 0.6188 loss: 0.5548 detection_loss_cls: 0.5548 2024/07/11 11:39:31 - mmengine - INFO - Iter(train) [ 20800/120000] base_lr: 1.8568e-04 lr: 1.8698e-05 eta: 3 days, 5:39:58 time: 2.8220 data_time: 0.1936 memory: 14080 grad_norm: 0.6189 loss: 0.5545 detection_loss_cls: 0.5545 2024/07/11 11:41:50 - mmengine - INFO - Iter(train) [ 20850/120000] base_lr: 1.8562e-04 lr: 1.8692e-05 eta: 3 days, 5:35:16 time: 2.8218 data_time: 0.1933 memory: 14080 grad_norm: 0.6187 loss: 0.5552 detection_loss_cls: 0.5552 2024/07/11 11:44:12 - mmengine - INFO - Iter(train) [ 20900/120000] base_lr: 1.8555e-04 lr: 1.8686e-05 eta: 3 days, 5:33:28 time: 2.8218 data_time: 0.1924 memory: 14080 grad_norm: 0.6186 loss: 0.5543 detection_loss_cls: 0.5543 2024/07/11 11:46:33 - mmengine - INFO - Iter(train) [ 20950/120000] base_lr: 1.8548e-04 lr: 1.8680e-05 eta: 3 days, 5:31:26 time: 2.8219 data_time: 0.1917 memory: 14080 grad_norm: 0.6184 loss: 0.5542 detection_loss_cls: 0.5542 2024/07/11 11:48:52 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 11:48:52 - mmengine - INFO - Iter(train) [ 21000/120000] base_lr: 1.8541e-04 lr: 1.8674e-05 eta: 3 days, 5:26:40 time: 2.8215 data_time: 0.1909 memory: 14080 grad_norm: 0.6168 loss: 0.5541 detection_loss_cls: 0.5541 2024/07/11 11:48:52 - mmengine - INFO - Saving checkpoint at 21000 iterations 2024/07/11 11:51:20 - mmengine - INFO - Iter(train) [ 21050/120000] base_lr: 1.8534e-04 lr: 1.8668e-05 eta: 3 days, 5:35:27 time: 2.8214 data_time: 0.1904 memory: 14080 grad_norm: 0.6166 loss: 0.5544 detection_loss_cls: 0.5544 2024/07/11 11:53:42 - mmengine - INFO - Iter(train) [ 21100/120000] base_lr: 1.8528e-04 lr: 1.8662e-05 eta: 3 days, 5:34:07 time: 2.8219 data_time: 0.1898 memory: 14080 grad_norm: 0.6165 loss: 0.5542 detection_loss_cls: 0.5542 2024/07/11 11:56:03 - mmengine - INFO - Iter(train) [ 21150/120000] base_lr: 1.8521e-04 lr: 1.8655e-05 eta: 3 days, 5:32:24 time: 2.8222 data_time: 0.1891 memory: 14080 grad_norm: 0.6167 loss: 0.5540 detection_loss_cls: 0.5540 2024/07/11 11:58:24 - mmengine - INFO - Iter(train) [ 21200/120000] base_lr: 1.8514e-04 lr: 1.8649e-05 eta: 3 days, 5:28:44 time: 2.8221 data_time: 0.1884 memory: 14080 grad_norm: 0.6166 loss: 0.5540 detection_loss_cls: 0.5540 2024/07/11 12:00:43 - mmengine - INFO - Iter(train) [ 21250/120000] base_lr: 1.8507e-04 lr: 1.8643e-05 eta: 3 days, 5:23:58 time: 2.8218 data_time: 0.1877 memory: 14080 grad_norm: 0.6167 loss: 0.5546 detection_loss_cls: 0.5546 2024/07/11 12:03:02 - mmengine - INFO - Iter(train) [ 21300/120000] base_lr: 1.8500e-04 lr: 1.8637e-05 eta: 3 days, 5:19:08 time: 2.8215 data_time: 0.1870 memory: 14080 grad_norm: 0.6166 loss: 0.5544 detection_loss_cls: 0.5544 2024/07/11 12:05:22 - mmengine - INFO - Iter(train) [ 21350/120000] base_lr: 1.8494e-04 lr: 1.8630e-05 eta: 3 days, 5:15:23 time: 2.8213 data_time: 0.1860 memory: 14080 grad_norm: 0.6165 loss: 0.5540 detection_loss_cls: 0.5540 2024/07/11 12:07:43 - 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mmengine - INFO - Saving checkpoint at 23000 iterations 2024/07/11 13:25:09 - mmengine - INFO - Iter(train) [ 23050/120000] base_lr: 1.8252e-04 lr: 1.8411e-05 eta: 3 days, 3:52:02 time: 2.8174 data_time: 0.1626 memory: 14080 grad_norm: 0.6128 loss: 0.5412 detection_loss_cls: 0.5412 2024/07/11 13:27:29 - mmengine - INFO - Iter(train) [ 23100/120000] base_lr: 1.8244e-04 lr: 1.8404e-05 eta: 3 days, 3:49:13 time: 2.8173 data_time: 0.1615 memory: 14080 grad_norm: 0.6127 loss: 0.5403 detection_loss_cls: 0.5403 2024/07/11 13:29:49 - mmengine - INFO - Iter(train) [ 23150/120000] base_lr: 1.8237e-04 lr: 1.8397e-05 eta: 3 days, 3:46:42 time: 2.8174 data_time: 0.1609 memory: 14080 grad_norm: 0.6124 loss: 0.5404 detection_loss_cls: 0.5404 2024/07/11 13:32:10 - mmengine - INFO - Iter(train) [ 23200/120000] base_lr: 1.8230e-04 lr: 1.8390e-05 eta: 3 days, 3:44:39 time: 2.8174 data_time: 0.1602 memory: 14080 grad_norm: 0.6122 loss: 0.5399 detection_loss_cls: 0.5399 2024/07/11 13:34:32 - mmengine - INFO - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 14:09:37 - mmengine - INFO - Iter(train) [ 24000/120000] base_lr: 1.8109e-04 lr: 1.8281e-05 eta: 3 days, 3:04:20 time: 2.8152 data_time: 0.1514 memory: 14080 grad_norm: 0.6122 loss: 0.5418 detection_loss_cls: 0.5418 2024/07/11 14:09:37 - mmengine - INFO - Saving checkpoint at 24000 iterations 2024/07/11 14:12:05 - mmengine - INFO - Iter(train) [ 24050/120000] base_lr: 1.8102e-04 lr: 1.8274e-05 eta: 3 days, 3:04:46 time: 2.8165 data_time: 0.1534 memory: 14080 grad_norm: 0.6123 loss: 0.5418 detection_loss_cls: 0.5418 2024/07/11 14:14:26 - mmengine - INFO - Iter(train) [ 24100/120000] base_lr: 1.8094e-04 lr: 1.8267e-05 eta: 3 days, 3:02:25 time: 2.8163 data_time: 0.1533 memory: 14080 grad_norm: 0.6124 loss: 0.5418 detection_loss_cls: 0.5418 2024/07/11 14:16:45 - mmengine - INFO - Iter(train) [ 24150/120000] base_lr: 1.8087e-04 lr: 1.8260e-05 eta: 3 days, 2:59:14 time: 2.8154 data_time: 0.1529 memory: 14080 grad_norm: 0.6124 loss: 0.5410 detection_loss_cls: 0.5410 2024/07/11 14:19:04 - 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mmengine - INFO - Iter(train) [ 24700/120000] base_lr: 1.8001e-04 lr: 1.8183e-05 eta: 3 days, 2:31:01 time: 2.8139 data_time: 0.1528 memory: 14080 grad_norm: 0.6103 loss: 0.5397 detection_loss_cls: 0.5397 2024/07/11 14:44:48 - mmengine - INFO - Iter(train) [ 24750/120000] base_lr: 1.7994e-04 lr: 1.8176e-05 eta: 3 days, 2:28:42 time: 2.8141 data_time: 0.1527 memory: 14080 grad_norm: 0.6102 loss: 0.5397 detection_loss_cls: 0.5397 2024/07/11 14:47:07 - mmengine - INFO - Iter(train) [ 24800/120000] base_lr: 1.7986e-04 lr: 1.8169e-05 eta: 3 days, 2:25:55 time: 2.8139 data_time: 0.1525 memory: 14080 grad_norm: 0.6100 loss: 0.5391 detection_loss_cls: 0.5391 2024/07/11 14:49:27 - mmengine - INFO - Iter(train) [ 24850/120000] base_lr: 1.7978e-04 lr: 1.8162e-05 eta: 3 days, 2:23:19 time: 2.8140 data_time: 0.1525 memory: 14080 grad_norm: 0.6100 loss: 0.5387 detection_loss_cls: 0.5387 2024/07/11 14:51:47 - mmengine - INFO - Iter(train) [ 24900/120000] base_lr: 1.7970e-04 lr: 1.8155e-05 eta: 3 days, 2:20:54 time: 2.8138 data_time: 0.1526 memory: 14080 grad_norm: 0.6099 loss: 0.5383 detection_loss_cls: 0.5383 2024/07/11 14:54:08 - mmengine - INFO - Iter(train) [ 24950/120000] base_lr: 1.7962e-04 lr: 1.8147e-05 eta: 3 days, 2:18:32 time: 2.8137 data_time: 0.1526 memory: 14080 grad_norm: 0.6097 loss: 0.5377 detection_loss_cls: 0.5377 2024/07/11 14:56:27 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 14:56:27 - mmengine - INFO - Iter(train) [ 25000/120000] base_lr: 1.7954e-04 lr: 1.8140e-05 eta: 3 days, 2:15:44 time: 2.8137 data_time: 0.1529 memory: 14080 grad_norm: 0.6096 loss: 0.5382 detection_loss_cls: 0.5382 2024/07/11 14:56:27 - mmengine - INFO - Saving checkpoint at 25000 iterations 2024/07/11 14:56:57 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:26 time: 0.4100 data_time: 0.0049 memory: 5704 2024/07/11 14:57:17 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:01 time: 0.4099 data_time: 0.0049 memory: 5703 2024/07/11 14:57:37 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:40 time: 0.4098 data_time: 0.0048 memory: 5703 2024/07/11 14:57:58 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:19 time: 0.4098 data_time: 0.0048 memory: 5703 2024/07/11 14:58:18 - mmengine - INFO - Iter(val) [250/834] eta: 0:03:59 time: 0.4098 data_time: 0.0048 memory: 5703 2024/07/11 14:58:38 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:38 time: 0.4097 data_time: 0.0048 memory: 5703 2024/07/11 14:58:59 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:17 time: 0.4096 data_time: 0.0048 memory: 5703 2024/07/11 14:59:19 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:57 time: 0.4096 data_time: 0.0048 memory: 5703 2024/07/11 14:59:39 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:36 time: 0.4096 data_time: 0.0048 memory: 5703 2024/07/11 15:00:00 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:16 time: 0.4097 data_time: 0.0048 memory: 5703 2024/07/11 15:00:21 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4096 data_time: 0.0048 memory: 5703 2024/07/11 15:00:41 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:35 time: 0.4097 data_time: 0.0048 memory: 5703 2024/07/11 15:01:02 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4097 data_time: 0.0048 memory: 5703 2024/07/11 15:01:22 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4097 data_time: 0.0048 memory: 5703 2024/07/11 15:01:42 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4096 data_time: 0.0048 memory: 5703 2024/07/11 15:02:03 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4096 data_time: 0.0048 memory: 5703 2024/07/11 15:02:19 - mmengine - INFO - Evaluating bbox... 2024/07/11 15:02:46 - mmengine - INFO - bbox_mAP_copypaste: 0.402 0.563 0.440 0.223 0.435 0.539 2024/07/11 15:02:46 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4020 coco/bbox_mAP_50: 0.5630 coco/bbox_mAP_75: 0.4400 coco/bbox_mAP_s: 0.2230 coco/bbox_mAP_m: 0.4350 coco/bbox_mAP_l: 0.5390 data_time: 0.0048 time: 0.4085 2024/07/11 15:05:05 - mmengine - INFO - Iter(train) [ 25050/120000] base_lr: 1.7946e-04 lr: 1.8133e-05 eta: 3 days, 2:22:08 time: 2.8188 data_time: 0.1587 memory: 14136 grad_norm: 0.6097 loss: 0.5385 detection_loss_cls: 0.5385 2024/07/11 15:07:24 - mmengine - INFO - Iter(train) [ 25100/120000] base_lr: 1.7939e-04 lr: 1.8126e-05 eta: 3 days, 2:19:11 time: 2.8181 data_time: 0.1593 memory: 14136 grad_norm: 0.6096 loss: 0.5397 detection_loss_cls: 0.5397 2024/07/11 15:09:44 - mmengine - INFO - Iter(train) [ 25150/120000] base_lr: 1.7931e-04 lr: 1.8119e-05 eta: 3 days, 2:16:29 time: 2.8177 data_time: 0.1596 memory: 14136 grad_norm: 0.6093 loss: 0.5398 detection_loss_cls: 0.5398 2024/07/11 15:12:04 - mmengine - INFO - Iter(train) [ 25200/120000] base_lr: 1.7923e-04 lr: 1.8112e-05 eta: 3 days, 2:13:48 time: 2.8176 data_time: 0.1600 memory: 14136 grad_norm: 0.6093 loss: 0.5398 detection_loss_cls: 0.5398 2024/07/11 15:14:24 - mmengine - INFO - Iter(train) [ 25250/120000] base_lr: 1.7915e-04 lr: 1.8104e-05 eta: 3 days, 2:11:07 time: 2.8177 data_time: 0.1606 memory: 14135 grad_norm: 0.6090 loss: 0.5404 detection_loss_cls: 0.5404 2024/07/11 15:16:43 - mmengine - INFO - Iter(train) [ 25300/120000] base_lr: 1.7907e-04 lr: 1.8097e-05 eta: 3 days, 2:08:26 time: 2.8179 data_time: 0.1610 memory: 14136 grad_norm: 0.6087 loss: 0.5405 detection_loss_cls: 0.5405 2024/07/11 15:19:04 - mmengine - INFO - Iter(train) [ 25350/120000] base_lr: 1.7899e-04 lr: 1.8090e-05 eta: 3 days, 2:06:00 time: 2.8181 data_time: 0.1619 memory: 14136 grad_norm: 0.6085 loss: 0.5416 detection_loss_cls: 0.5416 2024/07/11 15:21:24 - mmengine - INFO - Iter(train) [ 25400/120000] base_lr: 1.7891e-04 lr: 1.8083e-05 eta: 3 days, 2:03:14 time: 2.8177 data_time: 0.1620 memory: 14136 grad_norm: 0.6082 loss: 0.5421 detection_loss_cls: 0.5421 2024/07/11 15:23:43 - mmengine - INFO - Iter(train) [ 25450/120000] base_lr: 1.7883e-04 lr: 1.8075e-05 eta: 3 days, 2:00:19 time: 2.8173 data_time: 0.1621 memory: 14136 grad_norm: 0.6083 loss: 0.5417 detection_loss_cls: 0.5417 2024/07/11 15:26:02 - mmengine - INFO - Iter(train) [ 25500/120000] base_lr: 1.7875e-04 lr: 1.8068e-05 eta: 3 days, 1:57:31 time: 2.8173 data_time: 0.1620 memory: 14136 grad_norm: 0.6081 loss: 0.5414 detection_loss_cls: 0.5414 2024/07/11 15:28:21 - mmengine - INFO - Iter(train) [ 25550/120000] base_lr: 1.7867e-04 lr: 1.8061e-05 eta: 3 days, 1:54:38 time: 2.8169 data_time: 0.1622 memory: 14136 grad_norm: 0.6080 loss: 0.5416 detection_loss_cls: 0.5416 2024/07/11 15:30:39 - mmengine - INFO - Iter(train) [ 25600/120000] base_lr: 1.7859e-04 lr: 1.8053e-05 eta: 3 days, 1:51:39 time: 2.8164 data_time: 0.1623 memory: 14136 grad_norm: 0.6079 loss: 0.5416 detection_loss_cls: 0.5416 2024/07/11 15:32:59 - mmengine - INFO - Iter(train) [ 25650/120000] base_lr: 1.7851e-04 lr: 1.8046e-05 eta: 3 days, 1:48:54 time: 2.8163 data_time: 0.1622 memory: 14136 grad_norm: 0.6080 loss: 0.5417 detection_loss_cls: 0.5417 2024/07/11 15:35:19 - mmengine - INFO - Iter(train) [ 25700/120000] base_lr: 1.7843e-04 lr: 1.8039e-05 eta: 3 days, 1:46:19 time: 2.8166 data_time: 0.1624 memory: 14135 grad_norm: 0.6080 loss: 0.5411 detection_loss_cls: 0.5411 2024/07/11 15:37:38 - mmengine - INFO - Iter(train) [ 25750/120000] base_lr: 1.7835e-04 lr: 1.8031e-05 eta: 3 days, 1:43:32 time: 2.8166 data_time: 0.1625 memory: 14136 grad_norm: 0.6081 loss: 0.5412 detection_loss_cls: 0.5412 2024/07/11 15:39:58 - mmengine - INFO - Iter(train) [ 25800/120000] base_lr: 1.7826e-04 lr: 1.8024e-05 eta: 3 days, 1:40:58 time: 2.8165 data_time: 0.1628 memory: 14136 grad_norm: 0.6079 loss: 0.5413 detection_loss_cls: 0.5413 2024/07/11 15:42:18 - mmengine - INFO - Iter(train) [ 25850/120000] base_lr: 1.7818e-04 lr: 1.8017e-05 eta: 3 days, 1:38:18 time: 2.8165 data_time: 0.1630 memory: 14136 grad_norm: 0.6080 loss: 0.5416 detection_loss_cls: 0.5416 2024/07/11 15:44:39 - mmengine - INFO - Iter(train) [ 25900/120000] base_lr: 1.7810e-04 lr: 1.8009e-05 eta: 3 days, 1:36:06 time: 2.8165 data_time: 0.1632 memory: 14136 grad_norm: 0.6081 loss: 0.5415 detection_loss_cls: 0.5415 2024/07/11 15:46:58 - mmengine - INFO - Iter(train) [ 25950/120000] base_lr: 1.7802e-04 lr: 1.8002e-05 eta: 3 days, 1:33:15 time: 2.8160 data_time: 0.1633 memory: 14136 grad_norm: 0.6081 loss: 0.5418 detection_loss_cls: 0.5418 2024/07/11 15:49:18 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 15:49:18 - mmengine - INFO - Iter(train) [ 26000/120000] base_lr: 1.7794e-04 lr: 1.7994e-05 eta: 3 days, 1:30:39 time: 2.8158 data_time: 0.1632 memory: 14136 grad_norm: 0.6082 loss: 0.5413 detection_loss_cls: 0.5413 2024/07/11 15:49:18 - mmengine - INFO - Saving checkpoint at 26000 iterations 2024/07/11 15:51:45 - mmengine - INFO - Iter(train) [ 26050/120000] base_lr: 1.7786e-04 lr: 1.7987e-05 eta: 3 days, 1:29:59 time: 2.8154 data_time: 0.1635 memory: 14136 grad_norm: 0.6082 loss: 0.5410 detection_loss_cls: 0.5410 2024/07/11 15:54:04 - mmengine - INFO - Iter(train) [ 26100/120000] base_lr: 1.7778e-04 lr: 1.7980e-05 eta: 3 days, 1:27:06 time: 2.8150 data_time: 0.1636 memory: 14136 grad_norm: 0.6083 loss: 0.5410 detection_loss_cls: 0.5410 2024/07/11 15:56:23 - mmengine - INFO - Iter(train) [ 26150/120000] base_lr: 1.7769e-04 lr: 1.7972e-05 eta: 3 days, 1:24:16 time: 2.8151 data_time: 0.1637 memory: 14136 grad_norm: 0.6087 loss: 0.5409 detection_loss_cls: 0.5409 2024/07/11 15:58:43 - mmengine - INFO - Iter(train) [ 26200/120000] base_lr: 1.7761e-04 lr: 1.7965e-05 eta: 3 days, 1:21:48 time: 2.8150 data_time: 0.1636 memory: 14136 grad_norm: 0.6087 loss: 0.5403 detection_loss_cls: 0.5403 2024/07/11 16:01:02 - mmengine - INFO - Iter(train) [ 26250/120000] base_lr: 1.7753e-04 lr: 1.7957e-05 eta: 3 days, 1:19:06 time: 2.8144 data_time: 0.1636 memory: 14136 grad_norm: 0.6084 loss: 0.5405 detection_loss_cls: 0.5405 2024/07/11 16:03:21 - mmengine - INFO - Iter(train) [ 26300/120000] base_lr: 1.7745e-04 lr: 1.7950e-05 eta: 3 days, 1:16:11 time: 2.8140 data_time: 0.1635 memory: 14136 grad_norm: 0.6084 loss: 0.5400 detection_loss_cls: 0.5400 2024/07/11 16:05:41 - mmengine - INFO - Iter(train) [ 26350/120000] base_lr: 1.7736e-04 lr: 1.7942e-05 eta: 3 days, 1:13:39 time: 2.8140 data_time: 0.1639 memory: 14136 grad_norm: 0.6085 loss: 0.5405 detection_loss_cls: 0.5405 2024/07/11 16:08:00 - mmengine - INFO - Iter(train) [ 26400/120000] base_lr: 1.7728e-04 lr: 1.7935e-05 eta: 3 days, 1:10:48 time: 2.8133 data_time: 0.1640 memory: 14136 grad_norm: 0.6084 loss: 0.5403 detection_loss_cls: 0.5403 2024/07/11 16:10:18 - mmengine - INFO - Iter(train) [ 26450/120000] base_lr: 1.7720e-04 lr: 1.7927e-05 eta: 3 days, 1:08:01 time: 2.8131 data_time: 0.1645 memory: 14136 grad_norm: 0.6084 loss: 0.5405 detection_loss_cls: 0.5405 2024/07/11 16:12:38 - mmengine - INFO - Iter(train) [ 26500/120000] base_lr: 1.7712e-04 lr: 1.7920e-05 eta: 3 days, 1:05:22 time: 2.8129 data_time: 0.1644 memory: 14136 grad_norm: 0.6083 loss: 0.5400 detection_loss_cls: 0.5400 2024/07/11 16:14:58 - mmengine - INFO - Iter(train) [ 26550/120000] base_lr: 1.7703e-04 lr: 1.7912e-05 eta: 3 days, 1:02:53 time: 2.8127 data_time: 0.1645 memory: 14136 grad_norm: 0.6083 loss: 0.5402 detection_loss_cls: 0.5402 2024/07/11 16:17:17 - mmengine - INFO - Iter(train) [ 26600/120000] base_lr: 1.7695e-04 lr: 1.7905e-05 eta: 3 days, 1:00:12 time: 2.8123 data_time: 0.1645 memory: 14136 grad_norm: 0.6083 loss: 0.5399 detection_loss_cls: 0.5399 2024/07/11 16:19:36 - mmengine - INFO - Iter(train) [ 26650/120000] base_lr: 1.7687e-04 lr: 1.7897e-05 eta: 3 days, 0:57:26 time: 2.8118 data_time: 0.1648 memory: 14135 grad_norm: 0.6083 loss: 0.5402 detection_loss_cls: 0.5402 2024/07/11 16:21:55 - mmengine - INFO - Iter(train) [ 26700/120000] base_lr: 1.7678e-04 lr: 1.7890e-05 eta: 3 days, 0:54:40 time: 2.8114 data_time: 0.1651 memory: 14136 grad_norm: 0.6086 loss: 0.5409 detection_loss_cls: 0.5409 2024/07/11 16:24:14 - mmengine - INFO - Iter(train) [ 26750/120000] base_lr: 1.7670e-04 lr: 1.7882e-05 eta: 3 days, 0:52:01 time: 2.8116 data_time: 0.1652 memory: 14136 grad_norm: 0.6087 loss: 0.5408 detection_loss_cls: 0.5408 2024/07/11 16:26:33 - mmengine - INFO - Iter(train) [ 26800/120000] base_lr: 1.7662e-04 lr: 1.7874e-05 eta: 3 days, 0:49:10 time: 2.8110 data_time: 0.1653 memory: 14136 grad_norm: 0.6089 loss: 0.5408 detection_loss_cls: 0.5408 2024/07/11 16:28:52 - mmengine - INFO - Iter(train) [ 26850/120000] base_lr: 1.7653e-04 lr: 1.7867e-05 eta: 3 days, 0:46:28 time: 2.8106 data_time: 0.1653 memory: 14136 grad_norm: 0.6087 loss: 0.5407 detection_loss_cls: 0.5407 2024/07/11 16:31:12 - mmengine - INFO - Iter(train) [ 26900/120000] base_lr: 1.7645e-04 lr: 1.7859e-05 eta: 3 days, 0:43:57 time: 2.8101 data_time: 0.1652 memory: 14136 grad_norm: 0.6085 loss: 0.5409 detection_loss_cls: 0.5409 2024/07/11 16:33:32 - mmengine - INFO - Iter(train) [ 26950/120000] base_lr: 1.7637e-04 lr: 1.7851e-05 eta: 3 days, 0:41:25 time: 2.8103 data_time: 0.1650 memory: 14136 grad_norm: 0.6085 loss: 0.5404 detection_loss_cls: 0.5404 2024/07/11 16:35:51 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 16:35:51 - mmengine - INFO - Iter(train) [ 27000/120000] base_lr: 1.7628e-04 lr: 1.7844e-05 eta: 3 days, 0:38:51 time: 2.8102 data_time: 0.1647 memory: 14136 grad_norm: 0.6085 loss: 0.5398 detection_loss_cls: 0.5398 2024/07/11 16:35:51 - mmengine - INFO - Saving checkpoint at 27000 iterations 2024/07/11 16:38:20 - mmengine - INFO - Iter(train) [ 27050/120000] base_lr: 1.7620e-04 lr: 1.7836e-05 eta: 3 days, 0:38:12 time: 2.8103 data_time: 0.1649 memory: 14136 grad_norm: 0.6086 loss: 0.5394 detection_loss_cls: 0.5394 2024/07/11 16:40:39 - mmengine - INFO - Iter(train) [ 27100/120000] base_lr: 1.7611e-04 lr: 1.7828e-05 eta: 3 days, 0:35:29 time: 2.8101 data_time: 0.1651 memory: 14136 grad_norm: 0.6086 loss: 0.5395 detection_loss_cls: 0.5395 2024/07/11 16:42:58 - mmengine - INFO - Iter(train) [ 27150/120000] base_lr: 1.7603e-04 lr: 1.7821e-05 eta: 3 days, 0:32:52 time: 2.8098 data_time: 0.1648 memory: 14136 grad_norm: 0.6083 loss: 0.5385 detection_loss_cls: 0.5385 2024/07/11 16:45:19 - mmengine - INFO - Iter(train) [ 27200/120000] base_lr: 1.7594e-04 lr: 1.7813e-05 eta: 3 days, 0:30:36 time: 2.8097 data_time: 0.1651 memory: 14136 grad_norm: 0.6083 loss: 0.5389 detection_loss_cls: 0.5389 2024/07/11 16:47:38 - mmengine - INFO - Iter(train) [ 27250/120000] base_lr: 1.7586e-04 lr: 1.7805e-05 eta: 3 days, 0:27:50 time: 2.8091 data_time: 0.1653 memory: 14136 grad_norm: 0.6081 loss: 0.5386 detection_loss_cls: 0.5386 2024/07/11 16:49:58 - mmengine - INFO - Iter(train) [ 27300/120000] base_lr: 1.7577e-04 lr: 1.7798e-05 eta: 3 days, 0:25:23 time: 2.8091 data_time: 0.1653 memory: 14136 grad_norm: 0.6081 loss: 0.5379 detection_loss_cls: 0.5379 2024/07/11 16:52:17 - mmengine - INFO - Iter(train) [ 27350/120000] base_lr: 1.7569e-04 lr: 1.7790e-05 eta: 3 days, 0:22:50 time: 2.8090 data_time: 0.1655 memory: 14136 grad_norm: 0.6082 loss: 0.5373 detection_loss_cls: 0.5373 2024/07/11 16:54:38 - mmengine - INFO - Iter(train) [ 27400/120000] base_lr: 1.7560e-04 lr: 1.7782e-05 eta: 3 days, 0:20:29 time: 2.8086 data_time: 0.1656 memory: 14136 grad_norm: 0.6081 loss: 0.5373 detection_loss_cls: 0.5373 2024/07/11 16:56:58 - mmengine - INFO - Iter(train) [ 27450/120000] base_lr: 1.7552e-04 lr: 1.7774e-05 eta: 3 days, 0:18:00 time: 2.8085 data_time: 0.1660 memory: 14136 grad_norm: 0.6080 loss: 0.5383 detection_loss_cls: 0.5383 2024/07/11 16:59:17 - mmengine - INFO - Iter(train) [ 27500/120000] base_lr: 1.7543e-04 lr: 1.7767e-05 eta: 3 days, 0:15:20 time: 2.8080 data_time: 0.1657 memory: 14136 grad_norm: 0.6080 loss: 0.5371 detection_loss_cls: 0.5371 2024/07/11 17:01:37 - mmengine - INFO - Iter(train) [ 27550/120000] base_lr: 1.7535e-04 lr: 1.7759e-05 eta: 3 days, 0:12:48 time: 2.8080 data_time: 0.1657 memory: 14136 grad_norm: 0.6079 loss: 0.5372 detection_loss_cls: 0.5372 2024/07/11 17:03:57 - mmengine - INFO - Iter(train) [ 27600/120000] base_lr: 1.7526e-04 lr: 1.7751e-05 eta: 3 days, 0:10:20 time: 2.8078 data_time: 0.1658 memory: 14136 grad_norm: 0.6077 loss: 0.5368 detection_loss_cls: 0.5368 2024/07/11 17:06:16 - mmengine - INFO - Iter(train) [ 27650/120000] base_lr: 1.7518e-04 lr: 1.7743e-05 eta: 3 days, 0:07:41 time: 2.8076 data_time: 0.1660 memory: 14136 grad_norm: 0.6075 loss: 0.5369 detection_loss_cls: 0.5369 2024/07/11 17:08:36 - mmengine - INFO - Iter(train) [ 27700/120000] base_lr: 1.7509e-04 lr: 1.7736e-05 eta: 3 days, 0:05:22 time: 2.8076 data_time: 0.1661 memory: 14136 grad_norm: 0.6071 loss: 0.5364 detection_loss_cls: 0.5364 2024/07/11 17:10:55 - mmengine - INFO - Iter(train) [ 27750/120000] base_lr: 1.7501e-04 lr: 1.7728e-05 eta: 3 days, 0:02:33 time: 2.8072 data_time: 0.1664 memory: 14136 grad_norm: 0.6069 loss: 0.5368 detection_loss_cls: 0.5368 2024/07/11 17:13:13 - mmengine - INFO - Iter(train) [ 27800/120000] base_lr: 1.7492e-04 lr: 1.7720e-05 eta: 2 days, 23:59:50 time: 2.8068 data_time: 0.1663 memory: 14136 grad_norm: 0.6068 loss: 0.5364 detection_loss_cls: 0.5364 2024/07/11 17:15:33 - mmengine - INFO - Iter(train) [ 27850/120000] base_lr: 1.7483e-04 lr: 1.7712e-05 eta: 2 days, 23:57:19 time: 2.8069 data_time: 0.1660 memory: 14136 grad_norm: 0.6064 loss: 0.5361 detection_loss_cls: 0.5361 2024/07/11 17:17:52 - mmengine - INFO - Iter(train) [ 27900/120000] base_lr: 1.7475e-04 lr: 1.7704e-05 eta: 2 days, 23:54:43 time: 2.8065 data_time: 0.1658 memory: 14136 grad_norm: 0.6064 loss: 0.5354 detection_loss_cls: 0.5354 2024/07/11 17:20:11 - mmengine - INFO - Iter(train) [ 27950/120000] base_lr: 1.7466e-04 lr: 1.7696e-05 eta: 2 days, 23:51:57 time: 2.8060 data_time: 0.1656 memory: 14136 grad_norm: 0.6061 loss: 0.5350 detection_loss_cls: 0.5350 2024/07/11 17:22:31 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 17:22:31 - mmengine - INFO - Iter(train) [ 28000/120000] base_lr: 1.7457e-04 lr: 1.7688e-05 eta: 2 days, 23:49:29 time: 2.8059 data_time: 0.1655 memory: 14136 grad_norm: 0.6062 loss: 0.5349 detection_loss_cls: 0.5349 2024/07/11 17:22:31 - mmengine - INFO - Saving checkpoint at 28000 iterations 2024/07/11 17:24:58 - mmengine - INFO - Iter(train) [ 28050/120000] base_lr: 1.7449e-04 lr: 1.7681e-05 eta: 2 days, 23:48:29 time: 2.8058 data_time: 0.1655 memory: 14136 grad_norm: 0.6061 loss: 0.5344 detection_loss_cls: 0.5344 2024/07/11 17:27:16 - mmengine - INFO - Iter(train) [ 28100/120000] base_lr: 1.7440e-04 lr: 1.7673e-05 eta: 2 days, 23:45:35 time: 2.8050 data_time: 0.1657 memory: 14135 grad_norm: 0.6058 loss: 0.5346 detection_loss_cls: 0.5346 2024/07/11 17:29:36 - 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mmengine - INFO - Iter(train) [ 28400/120000] base_lr: 1.7388e-04 lr: 1.7625e-05 eta: 2 days, 23:29:41 time: 2.8036 data_time: 0.1656 memory: 14136 grad_norm: 0.6054 loss: 0.5342 detection_loss_cls: 0.5342 2024/07/11 17:43:29 - mmengine - INFO - Iter(train) [ 28450/120000] base_lr: 1.7379e-04 lr: 1.7617e-05 eta: 2 days, 23:27:11 time: 2.8034 data_time: 0.1654 memory: 14136 grad_norm: 0.6054 loss: 0.5338 detection_loss_cls: 0.5338 2024/07/11 17:45:47 - mmengine - INFO - Iter(train) [ 28500/120000] base_lr: 1.7370e-04 lr: 1.7609e-05 eta: 2 days, 23:24:24 time: 2.8026 data_time: 0.1652 memory: 14136 grad_norm: 0.6055 loss: 0.5338 detection_loss_cls: 0.5338 2024/07/11 17:48:06 - mmengine - INFO - Iter(train) [ 28550/120000] base_lr: 1.7361e-04 lr: 1.7601e-05 eta: 2 days, 23:21:51 time: 2.8024 data_time: 0.1650 memory: 14136 grad_norm: 0.6058 loss: 0.5333 detection_loss_cls: 0.5333 2024/07/11 17:50:25 - mmengine - INFO - Iter(train) [ 28600/120000] base_lr: 1.7352e-04 lr: 1.7593e-05 eta: 2 days, 23:19:12 time: 2.8021 data_time: 0.1649 memory: 14136 grad_norm: 0.6059 loss: 0.5332 detection_loss_cls: 0.5332 2024/07/11 17:52:44 - mmengine - INFO - Iter(train) [ 28650/120000] base_lr: 1.7344e-04 lr: 1.7585e-05 eta: 2 days, 23:16:42 time: 2.8018 data_time: 0.1653 memory: 14136 grad_norm: 0.6057 loss: 0.5340 detection_loss_cls: 0.5340 2024/07/11 17:55:02 - mmengine - INFO - Iter(train) [ 28700/120000] base_lr: 1.7335e-04 lr: 1.7577e-05 eta: 2 days, 23:13:53 time: 2.8014 data_time: 0.1653 memory: 14136 grad_norm: 0.6056 loss: 0.5341 detection_loss_cls: 0.5341 2024/07/11 17:57:21 - mmengine - INFO - Iter(train) [ 28750/120000] base_lr: 1.7326e-04 lr: 1.7569e-05 eta: 2 days, 23:11:20 time: 2.8010 data_time: 0.1652 memory: 14136 grad_norm: 0.6058 loss: 0.5335 detection_loss_cls: 0.5335 2024/07/11 17:59:40 - mmengine - INFO - Iter(train) [ 28800/120000] base_lr: 1.7317e-04 lr: 1.7561e-05 eta: 2 days, 23:08:42 time: 2.8008 data_time: 0.1654 memory: 14136 grad_norm: 0.6058 loss: 0.5337 detection_loss_cls: 0.5337 2024/07/11 18:01:59 - mmengine - INFO - Iter(train) [ 28850/120000] base_lr: 1.7308e-04 lr: 1.7553e-05 eta: 2 days, 23:06:10 time: 2.8006 data_time: 0.1655 memory: 14135 grad_norm: 0.6058 loss: 0.5338 detection_loss_cls: 0.5338 2024/07/11 18:04:19 - mmengine - INFO - Iter(train) [ 28900/120000] base_lr: 1.7299e-04 lr: 1.7545e-05 eta: 2 days, 23:03:46 time: 2.8005 data_time: 0.1654 memory: 14136 grad_norm: 0.6059 loss: 0.5336 detection_loss_cls: 0.5336 2024/07/11 18:06:39 - mmengine - INFO - Iter(train) [ 28950/120000] base_lr: 1.7290e-04 lr: 1.7537e-05 eta: 2 days, 23:01:22 time: 2.8004 data_time: 0.1655 memory: 14136 grad_norm: 0.6060 loss: 0.5342 detection_loss_cls: 0.5342 2024/07/11 18:08:58 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 18:08:58 - mmengine - INFO - Iter(train) [ 29000/120000] base_lr: 1.7281e-04 lr: 1.7529e-05 eta: 2 days, 22:58:45 time: 2.8002 data_time: 0.1653 memory: 14136 grad_norm: 0.6059 loss: 0.5335 detection_loss_cls: 0.5335 2024/07/11 18:08:58 - mmengine - INFO - Saving checkpoint at 29000 iterations 2024/07/11 18:11:25 - mmengine - INFO - Iter(train) [ 29050/120000] base_lr: 1.7272e-04 lr: 1.7520e-05 eta: 2 days, 22:57:28 time: 2.7948 data_time: 0.1592 memory: 14136 grad_norm: 0.6056 loss: 0.5323 detection_loss_cls: 0.5323 2024/07/11 18:13:44 - mmengine - INFO - Iter(train) [ 29100/120000] base_lr: 1.7264e-04 lr: 1.7512e-05 eta: 2 days, 22:54:55 time: 2.7948 data_time: 0.1591 memory: 14136 grad_norm: 0.6056 loss: 0.5316 detection_loss_cls: 0.5316 2024/07/11 18:16:03 - mmengine - INFO - Iter(train) [ 29150/120000] base_lr: 1.7255e-04 lr: 1.7504e-05 eta: 2 days, 22:52:17 time: 2.7945 data_time: 0.1588 memory: 14136 grad_norm: 0.6055 loss: 0.5312 detection_loss_cls: 0.5312 2024/07/11 18:18:22 - mmengine - INFO - Iter(train) [ 29200/120000] base_lr: 1.7246e-04 lr: 1.7496e-05 eta: 2 days, 22:49:43 time: 2.7943 data_time: 0.1587 memory: 14136 grad_norm: 0.6055 loss: 0.5309 detection_loss_cls: 0.5309 2024/07/11 18:20:40 - mmengine - INFO - Iter(train) [ 29250/120000] base_lr: 1.7237e-04 lr: 1.7488e-05 eta: 2 days, 22:47:03 time: 2.7940 data_time: 0.1581 memory: 14136 grad_norm: 0.6053 loss: 0.5293 detection_loss_cls: 0.5293 2024/07/11 18:23:00 - mmengine - INFO - Iter(train) [ 29300/120000] base_lr: 1.7228e-04 lr: 1.7480e-05 eta: 2 days, 22:44:38 time: 2.7940 data_time: 0.1584 memory: 14136 grad_norm: 0.6057 loss: 0.5301 detection_loss_cls: 0.5301 2024/07/11 18:23:47 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 18:25:20 - mmengine - INFO - Iter(train) [ 29350/120000] base_lr: 1.7219e-04 lr: 1.7471e-05 eta: 2 days, 22:42:07 time: 2.7937 data_time: 0.1578 memory: 14136 grad_norm: 0.6055 loss: 0.5286 detection_loss_cls: 0.5286 2024/07/11 18:27:40 - mmengine - INFO - Iter(train) [ 29400/120000] base_lr: 1.7210e-04 lr: 1.7463e-05 eta: 2 days, 22:39:47 time: 2.7939 data_time: 0.1582 memory: 14136 grad_norm: 0.6053 loss: 0.5290 detection_loss_cls: 0.5290 2024/07/11 18:29:58 - mmengine - INFO - Iter(train) [ 29450/120000] base_lr: 1.7201e-04 lr: 1.7455e-05 eta: 2 days, 22:37:06 time: 2.7937 data_time: 0.1578 memory: 14136 grad_norm: 0.6052 loss: 0.5284 detection_loss_cls: 0.5284 2024/07/11 18:32:18 - mmengine - INFO - Iter(train) [ 29500/120000] base_lr: 1.7192e-04 lr: 1.7447e-05 eta: 2 days, 22:34:41 time: 2.7938 data_time: 0.1578 memory: 14136 grad_norm: 0.6054 loss: 0.5287 detection_loss_cls: 0.5287 2024/07/11 18:34:37 - mmengine - INFO - Iter(train) [ 29550/120000] base_lr: 1.7183e-04 lr: 1.7439e-05 eta: 2 days, 22:32:09 time: 2.7939 data_time: 0.1576 memory: 14135 grad_norm: 0.6053 loss: 0.5281 detection_loss_cls: 0.5281 2024/07/11 18:36:55 - mmengine - INFO - Iter(train) [ 29600/120000] base_lr: 1.7173e-04 lr: 1.7430e-05 eta: 2 days, 22:29:23 time: 2.7937 data_time: 0.1577 memory: 14136 grad_norm: 0.6052 loss: 0.5283 detection_loss_cls: 0.5283 2024/07/11 18:39:15 - mmengine - INFO - Iter(train) [ 29650/120000] base_lr: 1.7164e-04 lr: 1.7422e-05 eta: 2 days, 22:26:58 time: 2.7938 data_time: 0.1579 memory: 14136 grad_norm: 0.6050 loss: 0.5285 detection_loss_cls: 0.5285 2024/07/11 18:41:35 - mmengine - INFO - Iter(train) [ 29700/120000] base_lr: 1.7155e-04 lr: 1.7414e-05 eta: 2 days, 22:24:36 time: 2.7938 data_time: 0.1579 memory: 14136 grad_norm: 0.6052 loss: 0.5291 detection_loss_cls: 0.5291 2024/07/11 18:43:55 - mmengine - INFO - Iter(train) [ 29750/120000] base_lr: 1.7146e-04 lr: 1.7406e-05 eta: 2 days, 22:22:12 time: 2.7940 data_time: 0.1578 memory: 14136 grad_norm: 0.6049 loss: 0.5291 detection_loss_cls: 0.5291 2024/07/11 18:46:14 - mmengine - INFO - Iter(train) [ 29800/120000] base_lr: 1.7137e-04 lr: 1.7397e-05 eta: 2 days, 22:19:37 time: 2.7937 data_time: 0.1572 memory: 14136 grad_norm: 0.6052 loss: 0.5283 detection_loss_cls: 0.5283 2024/07/11 18:48:34 - mmengine - INFO - Iter(train) [ 29850/120000] base_lr: 1.7128e-04 lr: 1.7389e-05 eta: 2 days, 22:17:12 time: 2.7938 data_time: 0.1572 memory: 14136 grad_norm: 0.6051 loss: 0.5287 detection_loss_cls: 0.5287 2024/07/11 18:50:52 - mmengine - INFO - Iter(train) [ 29900/120000] base_lr: 1.7119e-04 lr: 1.7381e-05 eta: 2 days, 22:14:39 time: 2.7932 data_time: 0.1570 memory: 14136 grad_norm: 0.6048 loss: 0.5286 detection_loss_cls: 0.5286 2024/07/11 18:53:12 - mmengine - INFO - Iter(train) [ 29950/120000] base_lr: 1.7110e-04 lr: 1.7372e-05 eta: 2 days, 22:12:13 time: 2.7934 data_time: 0.1570 memory: 14135 grad_norm: 0.6047 loss: 0.5289 detection_loss_cls: 0.5289 2024/07/11 18:55:31 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 18:55:31 - mmengine - INFO - Iter(train) [ 30000/120000] base_lr: 1.7101e-04 lr: 1.7364e-05 eta: 2 days, 22:09:43 time: 2.7932 data_time: 0.1572 memory: 14136 grad_norm: 0.6043 loss: 0.5295 detection_loss_cls: 0.5295 2024/07/11 18:55:31 - mmengine - INFO - Saving checkpoint at 30000 iterations 2024/07/11 18:56:00 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:26 time: 0.4096 data_time: 0.0048 memory: 5703 2024/07/11 18:56:21 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:04 time: 0.4097 data_time: 0.0048 memory: 5703 2024/07/11 18:56:42 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:42 time: 0.4097 data_time: 0.0048 memory: 5703 2024/07/11 18:57:02 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:22 time: 0.4097 data_time: 0.0048 memory: 5703 2024/07/11 18:57:23 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:01 time: 0.4098 data_time: 0.0048 memory: 5703 2024/07/11 18:57:43 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:40 time: 0.4098 data_time: 0.0048 memory: 5703 2024/07/11 18:58:04 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:19 time: 0.4098 data_time: 0.0048 memory: 5703 2024/07/11 18:58:25 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:59 time: 0.4099 data_time: 0.0048 memory: 5703 2024/07/11 18:58:45 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:38 time: 0.4099 data_time: 0.0048 memory: 5703 2024/07/11 18:59:06 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:17 time: 0.4099 data_time: 0.0048 memory: 5703 2024/07/11 18:59:26 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:57 time: 0.4099 data_time: 0.0048 memory: 5703 2024/07/11 18:59:47 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:36 time: 0.4099 data_time: 0.0048 memory: 5703 2024/07/11 19:00:07 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4099 data_time: 0.0048 memory: 5703 2024/07/11 19:00:28 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:55 time: 0.4100 data_time: 0.0047 memory: 5703 2024/07/11 19:00:49 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4102 data_time: 0.0047 memory: 5703 2024/07/11 19:01:09 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:14 time: 0.4103 data_time: 0.0047 memory: 5703 2024/07/11 19:01:26 - mmengine - INFO - Evaluating bbox... 2024/07/11 19:01:52 - mmengine - INFO - bbox_mAP_copypaste: 0.415 0.577 0.451 0.235 0.449 0.559 2024/07/11 19:01:52 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4150 coco/bbox_mAP_50: 0.5770 coco/bbox_mAP_75: 0.4510 coco/bbox_mAP_s: 0.2350 coco/bbox_mAP_m: 0.4490 coco/bbox_mAP_l: 0.5590 data_time: 0.0047 time: 0.4120 2024/07/11 19:04:10 - mmengine - INFO - Iter(train) [ 30050/120000] base_lr: 1.7091e-04 lr: 1.7356e-05 eta: 2 days, 22:11:23 time: 2.7982 data_time: 0.1622 memory: 14136 grad_norm: 0.6043 loss: 0.5294 detection_loss_cls: 0.5294 2024/07/11 19:06:31 - mmengine - INFO - Iter(train) [ 30100/120000] base_lr: 1.7082e-04 lr: 1.7347e-05 eta: 2 days, 22:09:06 time: 2.7988 data_time: 0.1623 memory: 14135 grad_norm: 0.6047 loss: 0.5293 detection_loss_cls: 0.5293 2024/07/11 19:08:51 - mmengine - INFO - Iter(train) [ 30150/120000] base_lr: 1.7073e-04 lr: 1.7339e-05 eta: 2 days, 22:06:42 time: 2.7990 data_time: 0.1625 memory: 14135 grad_norm: 0.6041 loss: 0.5298 detection_loss_cls: 0.5298 2024/07/11 19:11:12 - 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mmengine - INFO - Iter(train) [ 31900/120000] base_lr: 1.6744e-04 lr: 1.7040e-05 eta: 2 days, 20:43:08 time: 2.8024 data_time: 0.1635 memory: 14136 grad_norm: 0.6041 loss: 0.5253 detection_loss_cls: 0.5253 2024/07/11 20:32:54 - mmengine - INFO - Iter(train) [ 31950/120000] base_lr: 1.6734e-04 lr: 1.7031e-05 eta: 2 days, 20:40:42 time: 2.8027 data_time: 0.1638 memory: 14136 grad_norm: 0.6044 loss: 0.5255 detection_loss_cls: 0.5255 2024/07/11 20:35:13 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 20:35:13 - mmengine - INFO - Iter(train) [ 32000/120000] base_lr: 1.6725e-04 lr: 1.7022e-05 eta: 2 days, 20:38:14 time: 2.8026 data_time: 0.1638 memory: 14136 grad_norm: 0.6044 loss: 0.5256 detection_loss_cls: 0.5256 2024/07/11 20:35:13 - mmengine - INFO - Saving checkpoint at 32000 iterations 2024/07/11 20:37:41 - mmengine - INFO - Iter(train) [ 32050/120000] base_lr: 1.6715e-04 lr: 1.7014e-05 eta: 2 days, 20:36:48 time: 2.8027 data_time: 0.1642 memory: 14136 grad_norm: 0.6044 loss: 0.5261 detection_loss_cls: 0.5261 2024/07/11 20:40:01 - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 22:08:49 - mmengine - INFO - Iter(train) [ 34000/120000] base_lr: 1.6330e-04 lr: 1.6664e-05 eta: 2 days, 19:04:37 time: 2.8113 data_time: 0.1671 memory: 14136 grad_norm: 0.6026 loss: 0.5242 detection_loss_cls: 0.5242 2024/07/11 22:08:49 - mmengine - INFO - Saving checkpoint at 34000 iterations 2024/07/11 22:11:16 - mmengine - INFO - Iter(train) [ 34050/120000] base_lr: 1.6320e-04 lr: 1.6655e-05 eta: 2 days, 19:02:53 time: 2.8062 data_time: 0.1621 memory: 14136 grad_norm: 0.6024 loss: 0.5246 detection_loss_cls: 0.5246 2024/07/11 22:13:37 - mmengine - INFO - Iter(train) [ 34100/120000] base_lr: 1.6310e-04 lr: 1.6646e-05 eta: 2 days, 19:00:39 time: 2.8063 data_time: 0.1618 memory: 14136 grad_norm: 0.6017 loss: 0.5237 detection_loss_cls: 0.5237 2024/07/11 22:15:56 - mmengine - INFO - Iter(train) [ 34150/120000] base_lr: 1.6300e-04 lr: 1.6637e-05 eta: 2 days, 18:58:10 time: 2.8061 data_time: 0.1616 memory: 14135 grad_norm: 0.6018 loss: 0.5230 detection_loss_cls: 0.5230 2024/07/11 22:18:16 - mmengine - INFO - Iter(train) [ 34200/120000] base_lr: 1.6290e-04 lr: 1.6627e-05 eta: 2 days, 18:55:46 time: 2.8059 data_time: 0.1614 memory: 14136 grad_norm: 0.6015 loss: 0.5226 detection_loss_cls: 0.5226 2024/07/11 22:20:35 - mmengine - INFO - Iter(train) [ 34250/120000] base_lr: 1.6280e-04 lr: 1.6618e-05 eta: 2 days, 18:53:18 time: 2.8056 data_time: 0.1608 memory: 14136 grad_norm: 0.6017 loss: 0.5217 detection_loss_cls: 0.5217 2024/07/11 22:22:54 - mmengine - INFO - Iter(train) [ 34300/120000] base_lr: 1.6270e-04 lr: 1.6609e-05 eta: 2 days, 18:50:47 time: 2.8054 data_time: 0.1609 memory: 14136 grad_norm: 0.6015 loss: 0.5220 detection_loss_cls: 0.5220 2024/07/11 22:25:15 - mmengine - INFO - Iter(train) [ 34350/120000] base_lr: 1.6260e-04 lr: 1.6600e-05 eta: 2 days, 18:48:28 time: 2.8053 data_time: 0.1606 memory: 14136 grad_norm: 0.6013 loss: 0.5213 detection_loss_cls: 0.5213 2024/07/11 22:27:34 - mmengine - INFO - Iter(train) [ 34400/120000] base_lr: 1.6250e-04 lr: 1.6591e-05 eta: 2 days, 18:46:00 time: 2.8051 data_time: 0.1603 memory: 14136 grad_norm: 0.6010 loss: 0.5203 detection_loss_cls: 0.5203 2024/07/11 22:29:54 - mmengine - INFO - Iter(train) [ 34450/120000] base_lr: 1.6239e-04 lr: 1.6581e-05 eta: 2 days, 18:43:38 time: 2.8055 data_time: 0.1600 memory: 14136 grad_norm: 0.6008 loss: 0.5193 detection_loss_cls: 0.5193 2024/07/11 22:32:15 - mmengine - INFO - Iter(train) [ 34500/120000] base_lr: 1.6229e-04 lr: 1.6572e-05 eta: 2 days, 18:41:19 time: 2.8059 data_time: 0.1603 memory: 14136 grad_norm: 0.6010 loss: 0.5194 detection_loss_cls: 0.5194 2024/07/11 22:34:35 - mmengine - INFO - Iter(train) [ 34550/120000] base_lr: 1.6219e-04 lr: 1.6563e-05 eta: 2 days, 18:38:59 time: 2.8063 data_time: 0.1605 memory: 14136 grad_norm: 0.6012 loss: 0.5197 detection_loss_cls: 0.5197 2024/07/11 22:36:56 - mmengine - INFO - Iter(train) [ 34600/120000] base_lr: 1.6209e-04 lr: 1.6554e-05 eta: 2 days, 18:36:42 time: 2.8067 data_time: 0.1611 memory: 14136 grad_norm: 0.6012 loss: 0.5204 detection_loss_cls: 0.5204 2024/07/11 22:39:16 - mmengine - INFO - Iter(train) [ 34650/120000] base_lr: 1.6199e-04 lr: 1.6544e-05 eta: 2 days, 18:34:21 time: 2.8069 data_time: 0.1616 memory: 14135 grad_norm: 0.6013 loss: 0.5211 detection_loss_cls: 0.5211 2024/07/11 22:41:35 - mmengine - INFO - Iter(train) [ 34700/120000] base_lr: 1.6188e-04 lr: 1.6535e-05 eta: 2 days, 18:31:53 time: 2.8065 data_time: 0.1617 memory: 14136 grad_norm: 0.6013 loss: 0.5212 detection_loss_cls: 0.5212 2024/07/11 22:43:56 - mmengine - INFO - Iter(train) [ 34750/120000] base_lr: 1.6178e-04 lr: 1.6526e-05 eta: 2 days, 18:29:35 time: 2.8064 data_time: 0.1616 memory: 14136 grad_norm: 0.6013 loss: 0.5211 detection_loss_cls: 0.5211 2024/07/11 22:46:16 - mmengine - INFO - Iter(train) [ 34800/120000] base_lr: 1.6168e-04 lr: 1.6516e-05 eta: 2 days, 18:27:11 time: 2.8062 data_time: 0.1620 memory: 14136 grad_norm: 0.6015 loss: 0.5221 detection_loss_cls: 0.5221 2024/07/11 22:48:37 - mmengine - INFO - Iter(train) [ 34850/120000] base_lr: 1.6158e-04 lr: 1.6507e-05 eta: 2 days, 18:24:53 time: 2.8061 data_time: 0.1623 memory: 14136 grad_norm: 0.6016 loss: 0.5226 detection_loss_cls: 0.5226 2024/07/11 22:50:57 - mmengine - INFO - Iter(train) [ 34900/120000] base_lr: 1.6147e-04 lr: 1.6498e-05 eta: 2 days, 18:22:28 time: 2.8061 data_time: 0.1624 memory: 14136 grad_norm: 0.6012 loss: 0.5224 detection_loss_cls: 0.5224 2024/07/11 22:53:16 - mmengine - INFO - Iter(train) [ 34950/120000] base_lr: 1.6137e-04 lr: 1.6488e-05 eta: 2 days, 18:20:03 time: 2.8059 data_time: 0.1625 memory: 14136 grad_norm: 0.6010 loss: 0.5225 detection_loss_cls: 0.5225 2024/07/11 22:55:36 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 22:55:36 - mmengine - INFO - Iter(train) [ 35000/120000] base_lr: 1.6127e-04 lr: 1.6479e-05 eta: 2 days, 18:17:39 time: 2.8058 data_time: 0.1622 memory: 14136 grad_norm: 0.6009 loss: 0.5216 detection_loss_cls: 0.5216 2024/07/11 22:55:36 - mmengine - INFO - Saving checkpoint at 35000 iterations 2024/07/11 22:56:04 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:21 time: 0.4105 data_time: 0.0047 memory: 5703 2024/07/11 22:56:24 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:01 time: 0.4107 data_time: 0.0047 memory: 5703 2024/07/11 22:56:45 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:41 time: 0.4109 data_time: 0.0047 memory: 5703 2024/07/11 22:57:06 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:21 time: 0.4110 data_time: 0.0047 memory: 5703 2024/07/11 22:57:27 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:00 time: 0.4112 data_time: 0.0047 memory: 5703 2024/07/11 22:57:47 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:40 time: 0.4113 data_time: 0.0047 memory: 5703 2024/07/11 22:58:08 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:19 time: 0.4115 data_time: 0.0047 memory: 5703 2024/07/11 22:58:28 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:58 time: 0.4117 data_time: 0.0047 memory: 5703 2024/07/11 22:58:49 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:38 time: 0.4118 data_time: 0.0047 memory: 5703 2024/07/11 22:59:09 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:17 time: 0.4119 data_time: 0.0047 memory: 5703 2024/07/11 22:59:30 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4121 data_time: 0.0047 memory: 5703 2024/07/11 22:59:51 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:36 time: 0.4123 data_time: 0.0047 memory: 5703 2024/07/11 23:00:11 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4125 data_time: 0.0047 memory: 5703 2024/07/11 23:00:32 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:55 time: 0.4125 data_time: 0.0047 memory: 5703 2024/07/11 23:00:52 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4126 data_time: 0.0047 memory: 5703 2024/07/11 23:01:13 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:14 time: 0.4126 data_time: 0.0047 memory: 5703 2024/07/11 23:01:29 - mmengine - INFO - Evaluating bbox... 2024/07/11 23:01:55 - mmengine - INFO - bbox_mAP_copypaste: 0.425 0.590 0.463 0.240 0.458 0.573 2024/07/11 23:01:56 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4250 coco/bbox_mAP_50: 0.5900 coco/bbox_mAP_75: 0.4630 coco/bbox_mAP_s: 0.2400 coco/bbox_mAP_m: 0.4580 coco/bbox_mAP_l: 0.5730 data_time: 0.0046 time: 0.4117 2024/07/11 23:04:15 - mmengine - INFO - Iter(train) [ 35050/120000] base_lr: 1.6117e-04 lr: 1.6470e-05 eta: 2 days, 18:17:57 time: 2.8110 data_time: 0.1670 memory: 14136 grad_norm: 0.6011 loss: 0.5211 detection_loss_cls: 0.5211 2024/07/11 23:06:36 - mmengine - INFO - Iter(train) [ 35100/120000] base_lr: 1.6106e-04 lr: 1.6460e-05 eta: 2 days, 18:15:35 time: 2.8111 data_time: 0.1673 memory: 14136 grad_norm: 0.6014 loss: 0.5213 detection_loss_cls: 0.5213 2024/07/11 23:08:56 - mmengine - INFO - Iter(train) [ 35150/120000] base_lr: 1.6096e-04 lr: 1.6451e-05 eta: 2 days, 18:13:14 time: 2.8113 data_time: 0.1672 memory: 14136 grad_norm: 0.6017 loss: 0.5211 detection_loss_cls: 0.5211 2024/07/11 23:11:15 - mmengine - INFO - Iter(train) [ 35200/120000] base_lr: 1.6086e-04 lr: 1.6442e-05 eta: 2 days, 18:10:44 time: 2.8110 data_time: 0.1673 memory: 14136 grad_norm: 0.6016 loss: 0.5209 detection_loss_cls: 0.5209 2024/07/11 23:13:35 - mmengine - INFO - Iter(train) [ 35250/120000] base_lr: 1.6075e-04 lr: 1.6432e-05 eta: 2 days, 18:08:24 time: 2.8113 data_time: 0.1671 memory: 14136 grad_norm: 0.6018 loss: 0.5207 detection_loss_cls: 0.5207 2024/07/11 23:15:55 - mmengine - INFO - Iter(train) [ 35300/120000] base_lr: 1.6065e-04 lr: 1.6423e-05 eta: 2 days, 18:05:58 time: 2.8110 data_time: 0.1665 memory: 14135 grad_norm: 0.6016 loss: 0.5197 detection_loss_cls: 0.5197 2024/07/11 23:18:14 - mmengine - INFO - Iter(train) [ 35350/120000] base_lr: 1.6055e-04 lr: 1.6413e-05 eta: 2 days, 18:03:32 time: 2.8112 data_time: 0.1661 memory: 14136 grad_norm: 0.6015 loss: 0.5192 detection_loss_cls: 0.5192 2024/07/11 23:20:33 - mmengine - INFO - Iter(train) [ 35400/120000] base_lr: 1.6044e-04 lr: 1.6404e-05 eta: 2 days, 18:01:01 time: 2.8107 data_time: 0.1660 memory: 14136 grad_norm: 0.6012 loss: 0.5192 detection_loss_cls: 0.5192 2024/07/11 23:22:54 - mmengine - INFO - Iter(train) [ 35450/120000] base_lr: 1.6034e-04 lr: 1.6395e-05 eta: 2 days, 17:58:45 time: 2.8113 data_time: 0.1663 memory: 14136 grad_norm: 0.6011 loss: 0.5198 detection_loss_cls: 0.5198 2024/07/11 23:25:14 - mmengine - INFO - Iter(train) [ 35500/120000] base_lr: 1.6024e-04 lr: 1.6385e-05 eta: 2 days, 17:56:21 time: 2.8115 data_time: 0.1666 memory: 14136 grad_norm: 0.6010 loss: 0.5202 detection_loss_cls: 0.5202 2024/07/11 23:27:34 - mmengine - INFO - Iter(train) [ 35550/120000] base_lr: 1.6013e-04 lr: 1.6376e-05 eta: 2 days, 17:53:57 time: 2.8116 data_time: 0.1666 memory: 14136 grad_norm: 0.6009 loss: 0.5207 detection_loss_cls: 0.5207 2024/07/11 23:29:52 - mmengine - INFO - Iter(train) [ 35600/120000] base_lr: 1.6003e-04 lr: 1.6366e-05 eta: 2 days, 17:51:26 time: 2.8113 data_time: 0.1664 memory: 14136 grad_norm: 0.6008 loss: 0.5206 detection_loss_cls: 0.5206 2024/07/11 23:32:14 - mmengine - INFO - Iter(train) [ 35650/120000] base_lr: 1.5992e-04 lr: 1.6357e-05 eta: 2 days, 17:49:11 time: 2.8117 data_time: 0.1662 memory: 14135 grad_norm: 0.6013 loss: 0.5202 detection_loss_cls: 0.5202 2024/07/11 23:34:34 - mmengine - INFO - Iter(train) [ 35700/120000] base_lr: 1.5982e-04 lr: 1.6347e-05 eta: 2 days, 17:46:49 time: 2.8118 data_time: 0.1659 memory: 14136 grad_norm: 0.6011 loss: 0.5197 detection_loss_cls: 0.5197 2024/07/11 23:36:54 - mmengine - INFO - Iter(train) [ 35750/120000] base_lr: 1.5972e-04 lr: 1.6338e-05 eta: 2 days, 17:44:28 time: 2.8117 data_time: 0.1659 memory: 14136 grad_norm: 0.6012 loss: 0.5198 detection_loss_cls: 0.5198 2024/07/11 23:39:14 - mmengine - INFO - Iter(train) [ 35800/120000] base_lr: 1.5961e-04 lr: 1.6328e-05 eta: 2 days, 17:42:05 time: 2.8119 data_time: 0.1659 memory: 14136 grad_norm: 0.6013 loss: 0.5197 detection_loss_cls: 0.5197 2024/07/11 23:41:35 - mmengine - INFO - Iter(train) [ 35850/120000] base_lr: 1.5951e-04 lr: 1.6319e-05 eta: 2 days, 17:39:44 time: 2.8121 data_time: 0.1659 memory: 14135 grad_norm: 0.6009 loss: 0.5200 detection_loss_cls: 0.5200 2024/07/11 23:43:55 - mmengine - INFO - Iter(train) [ 35900/120000] base_lr: 1.5940e-04 lr: 1.6309e-05 eta: 2 days, 17:37:23 time: 2.8122 data_time: 0.1660 memory: 14136 grad_norm: 0.6002 loss: 0.5200 detection_loss_cls: 0.5200 2024/07/11 23:46:16 - mmengine - INFO - Iter(train) [ 35950/120000] base_lr: 1.5930e-04 lr: 1.6300e-05 eta: 2 days, 17:35:05 time: 2.8125 data_time: 0.1662 memory: 14136 grad_norm: 0.6000 loss: 0.5199 detection_loss_cls: 0.5199 2024/07/11 23:48:35 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/11 23:48:35 - mmengine - INFO - Iter(train) [ 36000/120000] base_lr: 1.5919e-04 lr: 1.6290e-05 eta: 2 days, 17:32:38 time: 2.8125 data_time: 0.1664 memory: 14136 grad_norm: 0.6000 loss: 0.5196 detection_loss_cls: 0.5196 2024/07/11 23:48:35 - mmengine - INFO - Saving checkpoint at 36000 iterations 2024/07/11 23:51:04 - mmengine - INFO - Iter(train) [ 36050/120000] base_lr: 1.5909e-04 lr: 1.6281e-05 eta: 2 days, 17:31:03 time: 2.8128 data_time: 0.1659 memory: 14136 grad_norm: 0.6000 loss: 0.5190 detection_loss_cls: 0.5190 2024/07/11 23:53:24 - mmengine - INFO - Iter(train) [ 36100/120000] base_lr: 1.5898e-04 lr: 1.6271e-05 eta: 2 days, 17:28:40 time: 2.8130 data_time: 0.1655 memory: 14136 grad_norm: 0.6000 loss: 0.5186 detection_loss_cls: 0.5186 2024/07/11 23:55:45 - mmengine - INFO - Iter(train) [ 36150/120000] base_lr: 1.5888e-04 lr: 1.6262e-05 eta: 2 days, 17:26:20 time: 2.8131 data_time: 0.1652 memory: 14135 grad_norm: 0.6000 loss: 0.5179 detection_loss_cls: 0.5179 2024/07/11 23:58:05 - mmengine - INFO - Iter(train) [ 36200/120000] base_lr: 1.5877e-04 lr: 1.6252e-05 eta: 2 days, 17:23:56 time: 2.8132 data_time: 0.1653 memory: 14136 grad_norm: 0.5999 loss: 0.5183 detection_loss_cls: 0.5183 2024/07/12 00:00:26 - mmengine - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 00:35:33 - mmengine - INFO - Iter(train) [ 37000/120000] base_lr: 1.5708e-04 lr: 1.6098e-05 eta: 2 days, 16:46:33 time: 2.8151 data_time: 0.1645 memory: 14136 grad_norm: 0.6003 loss: 0.5168 detection_loss_cls: 0.5168 2024/07/12 00:35:33 - mmengine - INFO - Saving checkpoint at 37000 iterations 2024/07/12 00:38:02 - mmengine - INFO - Iter(train) [ 37050/120000] base_lr: 1.5697e-04 lr: 1.6088e-05 eta: 2 days, 16:44:53 time: 2.8152 data_time: 0.1643 memory: 14136 grad_norm: 0.6006 loss: 0.5169 detection_loss_cls: 0.5169 2024/07/12 00:40:22 - mmengine - INFO - Iter(train) [ 37100/120000] base_lr: 1.5686e-04 lr: 1.6078e-05 eta: 2 days, 16:42:31 time: 2.8150 data_time: 0.1642 memory: 14136 grad_norm: 0.6007 loss: 0.5167 detection_loss_cls: 0.5167 2024/07/12 00:42:43 - mmengine - INFO - Iter(train) [ 37150/120000] base_lr: 1.5676e-04 lr: 1.6069e-05 eta: 2 days, 16:40:13 time: 2.8153 data_time: 0.1643 memory: 14136 grad_norm: 0.6007 loss: 0.5168 detection_loss_cls: 0.5168 2024/07/12 00:45:04 - 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mmengine - INFO - Iter(train) [ 37950/120000] base_lr: 1.5503e-04 lr: 1.5912e-05 eta: 2 days, 16:02:00 time: 2.8152 data_time: 0.1643 memory: 14136 grad_norm: 0.6010 loss: 0.5125 detection_loss_cls: 0.5125 2024/07/12 01:22:22 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 01:22:22 - mmengine - INFO - Iter(train) [ 38000/120000] base_lr: 1.5492e-04 lr: 1.5902e-05 eta: 2 days, 15:59:38 time: 2.8152 data_time: 0.1643 memory: 14136 grad_norm: 0.6011 loss: 0.5129 detection_loss_cls: 0.5129 2024/07/12 01:22:22 - mmengine - INFO - Saving checkpoint at 38000 iterations 2024/07/12 01:24:49 - mmengine - INFO - Iter(train) [ 38050/120000] base_lr: 1.5481e-04 lr: 1.5892e-05 eta: 2 days, 15:57:45 time: 2.8152 data_time: 0.1641 memory: 14136 grad_norm: 0.6013 loss: 0.5120 detection_loss_cls: 0.5120 2024/07/12 01:27:09 - mmengine - INFO - Iter(train) [ 38100/120000] base_lr: 1.5470e-04 lr: 1.5882e-05 eta: 2 days, 15:55:22 time: 2.8148 data_time: 0.1645 memory: 14136 grad_norm: 0.6016 loss: 0.5132 detection_loss_cls: 0.5132 2024/07/12 01:29:28 - 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mmengine - INFO - Iter(train) [ 39850/120000] base_lr: 1.5084e-04 lr: 1.5531e-05 eta: 2 days, 14:33:40 time: 2.8135 data_time: 0.1595 memory: 14136 grad_norm: 0.6012 loss: 0.5103 detection_loss_cls: 0.5103 2024/07/12 02:51:30 - mmengine - INFO - Iter(train) [ 39900/120000] base_lr: 1.5073e-04 lr: 1.5521e-05 eta: 2 days, 14:31:20 time: 2.8136 data_time: 0.1596 memory: 14135 grad_norm: 0.6016 loss: 0.5105 detection_loss_cls: 0.5105 2024/07/12 02:53:51 - mmengine - INFO - Iter(train) [ 39950/120000] base_lr: 1.5061e-04 lr: 1.5510e-05 eta: 2 days, 14:28:58 time: 2.8135 data_time: 0.1592 memory: 14136 grad_norm: 0.6016 loss: 0.5105 detection_loss_cls: 0.5105 2024/07/12 02:56:11 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 02:56:11 - mmengine - INFO - Iter(train) [ 40000/120000] base_lr: 1.5050e-04 lr: 1.5500e-05 eta: 2 days, 14:26:37 time: 2.8137 data_time: 0.1596 memory: 14136 grad_norm: 0.6015 loss: 0.5111 detection_loss_cls: 0.5111 2024/07/12 02:56:11 - mmengine - INFO - Saving checkpoint at 40000 iterations 2024/07/12 02:56:40 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:23 time: 0.4126 data_time: 0.0047 memory: 5703 2024/07/12 02:57:00 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:03 time: 0.4127 data_time: 0.0047 memory: 5703 2024/07/12 02:57:21 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:43 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 02:57:42 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:22 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 02:58:03 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:02 time: 0.4129 data_time: 0.0047 memory: 5703 2024/07/12 02:58:23 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:41 time: 0.4130 data_time: 0.0047 memory: 5703 2024/07/12 02:58:44 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:20 time: 0.4130 data_time: 0.0047 memory: 5703 2024/07/12 02:59:05 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:59 time: 0.4131 data_time: 0.0047 memory: 5703 2024/07/12 02:59:25 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:38 time: 0.4132 data_time: 0.0047 memory: 5703 2024/07/12 02:59:46 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:18 time: 0.4132 data_time: 0.0047 memory: 5703 2024/07/12 03:00:06 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:57 time: 0.4133 data_time: 0.0047 memory: 5703 2024/07/12 03:00:27 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:36 time: 0.4133 data_time: 0.0047 memory: 5703 2024/07/12 03:00:48 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:16 time: 0.4135 data_time: 0.0048 memory: 5703 2024/07/12 03:01:09 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:55 time: 0.4134 data_time: 0.0047 memory: 5703 2024/07/12 03:01:29 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4133 data_time: 0.0047 memory: 5703 2024/07/12 03:01:50 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:14 time: 0.4132 data_time: 0.0047 memory: 5703 2024/07/12 03:02:06 - mmengine - INFO - Evaluating bbox... 2024/07/12 03:02:33 - mmengine - INFO - bbox_mAP_copypaste: 0.433 0.598 0.471 0.247 0.469 0.582 2024/07/12 03:02:33 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4330 coco/bbox_mAP_50: 0.5980 coco/bbox_mAP_75: 0.4710 coco/bbox_mAP_s: 0.2470 coco/bbox_mAP_m: 0.4690 coco/bbox_mAP_l: 0.5820 data_time: 0.0048 time: 0.4136 2024/07/12 03:04:53 - mmengine - INFO - Iter(train) [ 40050/120000] base_lr: 1.5039e-04 lr: 1.5490e-05 eta: 2 days, 14:26:10 time: 2.8187 data_time: 0.1653 memory: 14135 grad_norm: 0.6015 loss: 0.5111 detection_loss_cls: 0.5111 2024/07/12 03:07:13 - mmengine - INFO - Iter(train) [ 40100/120000] base_lr: 1.5028e-04 lr: 1.5480e-05 eta: 2 days, 14:23:45 time: 2.8185 data_time: 0.1653 memory: 14136 grad_norm: 0.6015 loss: 0.5109 detection_loss_cls: 0.5109 2024/07/12 03:09:34 - mmengine - INFO - Iter(train) [ 40150/120000] base_lr: 1.5017e-04 lr: 1.5470e-05 eta: 2 days, 14:21:29 time: 2.8188 data_time: 0.1656 memory: 14136 grad_norm: 0.6014 loss: 0.5112 detection_loss_cls: 0.5112 2024/07/12 03:11:55 - mmengine - INFO - Iter(train) [ 40200/120000] base_lr: 1.5005e-04 lr: 1.5459e-05 eta: 2 days, 14:19:09 time: 2.8191 data_time: 0.1656 memory: 14135 grad_norm: 0.6014 loss: 0.5110 detection_loss_cls: 0.5110 2024/07/12 03:14:14 - mmengine - INFO - Iter(train) [ 40250/120000] base_lr: 1.4994e-04 lr: 1.5449e-05 eta: 2 days, 14:16:43 time: 2.8186 data_time: 0.1657 memory: 14136 grad_norm: 0.6011 loss: 0.5110 detection_loss_cls: 0.5110 2024/07/12 03:16:33 - mmengine - INFO - Iter(train) [ 40300/120000] base_lr: 1.4983e-04 lr: 1.5439e-05 eta: 2 days, 14:14:17 time: 2.8184 data_time: 0.1660 memory: 14136 grad_norm: 0.6016 loss: 0.5113 detection_loss_cls: 0.5113 2024/07/12 03:18:54 - mmengine - INFO - Iter(train) [ 40350/120000] base_lr: 1.4971e-04 lr: 1.5429e-05 eta: 2 days, 14:11:55 time: 2.8184 data_time: 0.1661 memory: 14136 grad_norm: 0.6021 loss: 0.5114 detection_loss_cls: 0.5114 2024/07/12 03:21:14 - mmengine - INFO - Iter(train) [ 40400/120000] base_lr: 1.4960e-04 lr: 1.5418e-05 eta: 2 days, 14:09:34 time: 2.8184 data_time: 0.1663 memory: 14136 grad_norm: 0.6021 loss: 0.5114 detection_loss_cls: 0.5114 2024/07/12 03:23:32 - mmengine - INFO - Iter(train) [ 40450/120000] base_lr: 1.4949e-04 lr: 1.5408e-05 eta: 2 days, 14:07:01 time: 2.8176 data_time: 0.1667 memory: 14136 grad_norm: 0.6018 loss: 0.5117 detection_loss_cls: 0.5117 2024/07/12 03:25:50 - mmengine - INFO - Iter(train) [ 40500/120000] base_lr: 1.4938e-04 lr: 1.5398e-05 eta: 2 days, 14:04:34 time: 2.8170 data_time: 0.1666 memory: 14136 grad_norm: 0.6017 loss: 0.5116 detection_loss_cls: 0.5116 2024/07/12 03:28:11 - mmengine - INFO - Iter(train) [ 40550/120000] base_lr: 1.4926e-04 lr: 1.5388e-05 eta: 2 days, 14:02:12 time: 2.8169 data_time: 0.1673 memory: 14136 grad_norm: 0.6017 loss: 0.5125 detection_loss_cls: 0.5125 2024/07/12 03:30:31 - mmengine - INFO - Iter(train) [ 40600/120000] base_lr: 1.4915e-04 lr: 1.5377e-05 eta: 2 days, 13:59:50 time: 2.8169 data_time: 0.1677 memory: 14136 grad_norm: 0.6018 loss: 0.5129 detection_loss_cls: 0.5129 2024/07/12 03:32:51 - mmengine - INFO - Iter(train) [ 40650/120000] base_lr: 1.4904e-04 lr: 1.5367e-05 eta: 2 days, 13:57:30 time: 2.8168 data_time: 0.1674 memory: 14136 grad_norm: 0.6020 loss: 0.5120 detection_loss_cls: 0.5120 2024/07/12 03:35:12 - mmengine - INFO - Iter(train) [ 40700/120000] base_lr: 1.4892e-04 lr: 1.5357e-05 eta: 2 days, 13:55:09 time: 2.8169 data_time: 0.1678 memory: 14136 grad_norm: 0.6018 loss: 0.5122 detection_loss_cls: 0.5122 2024/07/12 03:37:32 - mmengine - INFO - Iter(train) [ 40750/120000] base_lr: 1.4881e-04 lr: 1.5346e-05 eta: 2 days, 13:52:46 time: 2.8167 data_time: 0.1682 memory: 14136 grad_norm: 0.6018 loss: 0.5130 detection_loss_cls: 0.5130 2024/07/12 03:39:53 - mmengine - INFO - Iter(train) [ 40800/120000] base_lr: 1.4870e-04 lr: 1.5336e-05 eta: 2 days, 13:50:27 time: 2.8167 data_time: 0.1684 memory: 14136 grad_norm: 0.6015 loss: 0.5135 detection_loss_cls: 0.5135 2024/07/12 03:42:13 - mmengine - INFO - Iter(train) [ 40850/120000] base_lr: 1.4858e-04 lr: 1.5326e-05 eta: 2 days, 13:48:05 time: 2.8164 data_time: 0.1684 memory: 14136 grad_norm: 0.6017 loss: 0.5136 detection_loss_cls: 0.5136 2024/07/12 03:44:32 - mmengine - INFO - Iter(train) [ 40900/120000] base_lr: 1.4847e-04 lr: 1.5315e-05 eta: 2 days, 13:45:37 time: 2.8158 data_time: 0.1677 memory: 14136 grad_norm: 0.6014 loss: 0.5119 detection_loss_cls: 0.5119 2024/07/12 03:46:51 - mmengine - INFO - Iter(train) [ 40950/120000] base_lr: 1.4835e-04 lr: 1.5305e-05 eta: 2 days, 13:43:14 time: 2.8158 data_time: 0.1678 memory: 14136 grad_norm: 0.6014 loss: 0.5121 detection_loss_cls: 0.5121 2024/07/12 03:49:11 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 03:49:11 - mmengine - INFO - Iter(train) [ 41000/120000] base_lr: 1.4824e-04 lr: 1.5295e-05 eta: 2 days, 13:40:50 time: 2.8159 data_time: 0.1678 memory: 14136 grad_norm: 0.6011 loss: 0.5123 detection_loss_cls: 0.5123 2024/07/12 03:49:11 - mmengine - INFO - Saving checkpoint at 41000 iterations 2024/07/12 03:51:39 - mmengine - INFO - Iter(train) [ 41050/120000] base_lr: 1.4813e-04 lr: 1.5284e-05 eta: 2 days, 13:38:58 time: 2.8157 data_time: 0.1679 memory: 14136 grad_norm: 0.6011 loss: 0.5116 detection_loss_cls: 0.5116 2024/07/12 03:53:59 - mmengine - INFO - Iter(train) [ 41100/120000] base_lr: 1.4801e-04 lr: 1.5274e-05 eta: 2 days, 13:36:36 time: 2.8157 data_time: 0.1679 memory: 14135 grad_norm: 0.6011 loss: 0.5115 detection_loss_cls: 0.5115 2024/07/12 03:56:20 - mmengine - INFO - Iter(train) [ 41150/120000] base_lr: 1.4790e-04 lr: 1.5264e-05 eta: 2 days, 13:34:14 time: 2.8156 data_time: 0.1675 memory: 14136 grad_norm: 0.6009 loss: 0.5107 detection_loss_cls: 0.5107 2024/07/12 03:58:39 - mmengine - INFO - Iter(train) [ 41200/120000] base_lr: 1.4778e-04 lr: 1.5253e-05 eta: 2 days, 13:31:49 time: 2.8152 data_time: 0.1672 memory: 14136 grad_norm: 0.6010 loss: 0.5107 detection_loss_cls: 0.5107 2024/07/12 04:01:00 - mmengine - INFO - Iter(train) [ 41250/120000] base_lr: 1.4767e-04 lr: 1.5243e-05 eta: 2 days, 13:29:31 time: 2.8155 data_time: 0.1672 memory: 14135 grad_norm: 0.6008 loss: 0.5104 detection_loss_cls: 0.5104 2024/07/12 04:03:19 - mmengine - INFO - Iter(train) [ 41300/120000] base_lr: 1.4756e-04 lr: 1.5232e-05 eta: 2 days, 13:27:06 time: 2.8153 data_time: 0.1671 memory: 14136 grad_norm: 0.6005 loss: 0.5103 detection_loss_cls: 0.5103 2024/07/12 04:05:40 - mmengine - INFO - Iter(train) [ 41350/120000] base_lr: 1.4744e-04 lr: 1.5222e-05 eta: 2 days, 13:24:44 time: 2.8157 data_time: 0.1672 memory: 14135 grad_norm: 0.6005 loss: 0.5103 detection_loss_cls: 0.5103 2024/07/12 04:08:00 - mmengine - INFO - Iter(train) [ 41400/120000] base_lr: 1.4733e-04 lr: 1.5212e-05 eta: 2 days, 13:22:23 time: 2.8157 data_time: 0.1673 memory: 14136 grad_norm: 0.6002 loss: 0.5105 detection_loss_cls: 0.5105 2024/07/12 04:10:19 - mmengine - INFO - Iter(train) [ 41450/120000] base_lr: 1.4721e-04 lr: 1.5201e-05 eta: 2 days, 13:19:57 time: 2.8155 data_time: 0.1678 memory: 14136 grad_norm: 0.6005 loss: 0.5111 detection_loss_cls: 0.5111 2024/07/12 04:12:39 - mmengine - INFO - Iter(train) [ 41500/120000] base_lr: 1.4710e-04 lr: 1.5191e-05 eta: 2 days, 13:17:32 time: 2.8153 data_time: 0.1679 memory: 14136 grad_norm: 0.6005 loss: 0.5112 detection_loss_cls: 0.5112 2024/07/12 04:14:57 - mmengine - INFO - Iter(train) [ 41550/120000] base_lr: 1.4698e-04 lr: 1.5180e-05 eta: 2 days, 13:15:05 time: 2.8148 data_time: 0.1678 memory: 14136 grad_norm: 0.6002 loss: 0.5106 detection_loss_cls: 0.5106 2024/07/12 04:17:17 - mmengine - INFO - Iter(train) [ 41600/120000] base_lr: 1.4687e-04 lr: 1.5170e-05 eta: 2 days, 13:12:40 time: 2.8148 data_time: 0.1679 memory: 14135 grad_norm: 0.6003 loss: 0.5105 detection_loss_cls: 0.5105 2024/07/12 04:19:36 - mmengine - INFO - Iter(train) [ 41650/120000] base_lr: 1.4675e-04 lr: 1.5159e-05 eta: 2 days, 13:10:16 time: 2.8148 data_time: 0.1680 memory: 14136 grad_norm: 0.6005 loss: 0.5101 detection_loss_cls: 0.5101 2024/07/12 04:21:54 - mmengine - INFO - Iter(train) [ 41700/120000] base_lr: 1.4664e-04 lr: 1.5149e-05 eta: 2 days, 13:07:46 time: 2.8140 data_time: 0.1679 memory: 14136 grad_norm: 0.6004 loss: 0.5100 detection_loss_cls: 0.5100 2024/07/12 04:24:13 - mmengine - INFO - Iter(train) [ 41750/120000] base_lr: 1.4652e-04 lr: 1.5139e-05 eta: 2 days, 13:05:21 time: 2.8139 data_time: 0.1676 memory: 14136 grad_norm: 0.6003 loss: 0.5092 detection_loss_cls: 0.5092 2024/07/12 04:26:33 - mmengine - INFO - Iter(train) [ 41800/120000] base_lr: 1.4641e-04 lr: 1.5128e-05 eta: 2 days, 13:02:58 time: 2.8143 data_time: 0.1683 memory: 14136 grad_norm: 0.6001 loss: 0.5105 detection_loss_cls: 0.5105 2024/07/12 04:28:53 - mmengine - INFO - Iter(train) [ 41850/120000] base_lr: 1.4629e-04 lr: 1.5118e-05 eta: 2 days, 13:00:34 time: 2.8143 data_time: 0.1683 memory: 14136 grad_norm: 0.6001 loss: 0.5101 detection_loss_cls: 0.5101 2024/07/12 04:31:12 - mmengine - INFO - Iter(train) [ 41900/120000] base_lr: 1.4618e-04 lr: 1.5107e-05 eta: 2 days, 12:58:09 time: 2.8141 data_time: 0.1685 memory: 14136 grad_norm: 0.5999 loss: 0.5100 detection_loss_cls: 0.5100 2024/07/12 04:33:30 - mmengine - INFO - Iter(train) [ 41950/120000] base_lr: 1.4606e-04 lr: 1.5097e-05 eta: 2 days, 12:55:39 time: 2.8135 data_time: 0.1684 memory: 14136 grad_norm: 0.5996 loss: 0.5101 detection_loss_cls: 0.5101 2024/07/12 04:35:50 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 04:35:50 - mmengine - INFO - Iter(train) [ 42000/120000] base_lr: 1.4595e-04 lr: 1.5086e-05 eta: 2 days, 12:53:17 time: 2.8135 data_time: 0.1686 memory: 14136 grad_norm: 0.5994 loss: 0.5102 detection_loss_cls: 0.5102 2024/07/12 04:35:50 - mmengine - INFO - Saving checkpoint at 42000 iterations 2024/07/12 04:38:17 - mmengine - INFO - Iter(train) [ 42050/120000] base_lr: 1.4583e-04 lr: 1.5076e-05 eta: 2 days, 12:51:20 time: 2.8137 data_time: 0.1688 memory: 14136 grad_norm: 0.5992 loss: 0.5105 detection_loss_cls: 0.5105 2024/07/12 04:40:37 - mmengine - INFO - Iter(train) [ 42100/120000] base_lr: 1.4572e-04 lr: 1.5065e-05 eta: 2 days, 12:48:55 time: 2.8135 data_time: 0.1686 memory: 14136 grad_norm: 0.5990 loss: 0.5099 detection_loss_cls: 0.5099 2024/07/12 04:42:56 - mmengine - INFO - Iter(train) [ 42150/120000] base_lr: 1.4560e-04 lr: 1.5055e-05 eta: 2 days, 12:46:31 time: 2.8135 data_time: 0.1687 memory: 14136 grad_norm: 0.5990 loss: 0.5099 detection_loss_cls: 0.5099 2024/07/12 04:45:16 - mmengine - INFO - Iter(train) [ 42200/120000] base_lr: 1.4548e-04 lr: 1.5044e-05 eta: 2 days, 12:44:10 time: 2.8133 data_time: 0.1687 memory: 14135 grad_norm: 0.5990 loss: 0.5096 detection_loss_cls: 0.5096 2024/07/12 04:47:36 - mmengine - INFO - Iter(train) [ 42250/120000] base_lr: 1.4537e-04 lr: 1.5034e-05 eta: 2 days, 12:41:45 time: 2.8129 data_time: 0.1684 memory: 14135 grad_norm: 0.5988 loss: 0.5090 detection_loss_cls: 0.5090 2024/07/12 04:49:57 - mmengine - INFO - Iter(train) [ 42300/120000] base_lr: 1.4525e-04 lr: 1.5023e-05 eta: 2 days, 12:39:26 time: 2.8128 data_time: 0.1683 memory: 14136 grad_norm: 0.5987 loss: 0.5084 detection_loss_cls: 0.5084 2024/07/12 04:52:16 - 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mmengine - INFO - Iter(train) [ 42600/120000] base_lr: 1.4456e-04 lr: 1.4960e-05 eta: 2 days, 12:24:54 time: 2.8112 data_time: 0.1697 memory: 14136 grad_norm: 0.5986 loss: 0.5092 detection_loss_cls: 0.5092 2024/07/12 05:06:12 - mmengine - INFO - Iter(train) [ 42650/120000] base_lr: 1.4444e-04 lr: 1.4949e-05 eta: 2 days, 12:22:33 time: 2.8109 data_time: 0.1696 memory: 14136 grad_norm: 0.5988 loss: 0.5090 detection_loss_cls: 0.5090 2024/07/12 05:08:32 - mmengine - INFO - Iter(train) [ 42700/120000] base_lr: 1.4432e-04 lr: 1.4938e-05 eta: 2 days, 12:20:11 time: 2.8112 data_time: 0.1699 memory: 14136 grad_norm: 0.5990 loss: 0.5093 detection_loss_cls: 0.5093 2024/07/12 05:10:52 - mmengine - INFO - Iter(train) [ 42750/120000] base_lr: 1.4421e-04 lr: 1.4928e-05 eta: 2 days, 12:17:50 time: 2.8113 data_time: 0.1701 memory: 14136 grad_norm: 0.5989 loss: 0.5091 detection_loss_cls: 0.5091 2024/07/12 05:13:10 - mmengine - INFO - Iter(train) [ 42800/120000] base_lr: 1.4409e-04 lr: 1.4917e-05 eta: 2 days, 12:15:21 time: 2.8105 data_time: 0.1702 memory: 14136 grad_norm: 0.5991 loss: 0.5093 detection_loss_cls: 0.5093 2024/07/12 05:15:30 - mmengine - INFO - Iter(train) [ 42850/120000] base_lr: 1.4397e-04 lr: 1.4907e-05 eta: 2 days, 12:12:57 time: 2.8104 data_time: 0.1705 memory: 14136 grad_norm: 0.5991 loss: 0.5095 detection_loss_cls: 0.5095 2024/07/12 05:17:48 - mmengine - INFO - Iter(train) [ 42900/120000] base_lr: 1.4386e-04 lr: 1.4896e-05 eta: 2 days, 12:10:30 time: 2.8100 data_time: 0.1706 memory: 14136 grad_norm: 0.5993 loss: 0.5093 detection_loss_cls: 0.5093 2024/07/12 05:20:08 - mmengine - INFO - Iter(train) [ 42950/120000] base_lr: 1.4374e-04 lr: 1.4885e-05 eta: 2 days, 12:08:07 time: 2.8097 data_time: 0.1706 memory: 14135 grad_norm: 0.5990 loss: 0.5094 detection_loss_cls: 0.5094 2024/07/12 05:22:29 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 05:22:29 - mmengine - INFO - Iter(train) [ 43000/120000] base_lr: 1.4362e-04 lr: 1.4875e-05 eta: 2 days, 12:05:47 time: 2.8096 data_time: 0.1705 memory: 14136 grad_norm: 0.5991 loss: 0.5090 detection_loss_cls: 0.5090 2024/07/12 05:22:29 - mmengine - INFO - Saving checkpoint at 43000 iterations 2024/07/12 05:24:56 - mmengine - INFO - Iter(train) [ 43050/120000] base_lr: 1.4351e-04 lr: 1.4864e-05 eta: 2 days, 12:03:50 time: 2.8094 data_time: 0.1705 memory: 14136 grad_norm: 0.5991 loss: 0.5092 detection_loss_cls: 0.5092 2024/07/12 05:27:15 - mmengine - INFO - Iter(train) [ 43100/120000] base_lr: 1.4339e-04 lr: 1.4854e-05 eta: 2 days, 12:01:23 time: 2.8087 data_time: 0.1706 memory: 14136 grad_norm: 0.5989 loss: 0.5092 detection_loss_cls: 0.5092 2024/07/12 05:29:35 - mmengine - INFO - Iter(train) [ 43150/120000] base_lr: 1.4327e-04 lr: 1.4843e-05 eta: 2 days, 11:59:01 time: 2.8091 data_time: 0.1709 memory: 14136 grad_norm: 0.5988 loss: 0.5099 detection_loss_cls: 0.5099 2024/07/12 05:31:54 - mmengine - INFO - Iter(train) [ 43200/120000] base_lr: 1.4315e-04 lr: 1.4832e-05 eta: 2 days, 11:56:37 time: 2.8089 data_time: 0.1712 memory: 14136 grad_norm: 0.5992 loss: 0.5103 detection_loss_cls: 0.5103 2024/07/12 05:34:14 - mmengine - INFO - Iter(train) [ 43250/120000] base_lr: 1.4304e-04 lr: 1.4822e-05 eta: 2 days, 11:54:14 time: 2.8087 data_time: 0.1713 memory: 14136 grad_norm: 0.5992 loss: 0.5109 detection_loss_cls: 0.5109 2024/07/12 05:36:33 - mmengine - INFO - Iter(train) [ 43300/120000] base_lr: 1.4292e-04 lr: 1.4811e-05 eta: 2 days, 11:51:50 time: 2.8087 data_time: 0.1713 memory: 14136 grad_norm: 0.5996 loss: 0.5110 detection_loss_cls: 0.5110 2024/07/12 05:38:53 - mmengine - INFO - Iter(train) [ 43350/120000] base_lr: 1.4280e-04 lr: 1.4800e-05 eta: 2 days, 11:49:25 time: 2.8083 data_time: 0.1713 memory: 14136 grad_norm: 0.5996 loss: 0.5108 detection_loss_cls: 0.5108 2024/07/12 05:41:11 - mmengine - INFO - Iter(train) [ 43400/120000] base_lr: 1.4268e-04 lr: 1.4790e-05 eta: 2 days, 11:46:57 time: 2.8078 data_time: 0.1715 memory: 14136 grad_norm: 0.5996 loss: 0.5107 detection_loss_cls: 0.5107 2024/07/12 05:43:30 - mmengine - INFO - Iter(train) [ 43450/120000] base_lr: 1.4257e-04 lr: 1.4779e-05 eta: 2 days, 11:44:32 time: 2.8077 data_time: 0.1719 memory: 14135 grad_norm: 0.5994 loss: 0.5112 detection_loss_cls: 0.5112 2024/07/12 05:45:50 - mmengine - INFO - Iter(train) [ 43500/120000] base_lr: 1.4245e-04 lr: 1.4768e-05 eta: 2 days, 11:42:09 time: 2.8075 data_time: 0.1717 memory: 14135 grad_norm: 0.5991 loss: 0.5110 detection_loss_cls: 0.5110 2024/07/12 05:48:10 - mmengine - INFO - Iter(train) [ 43550/120000] base_lr: 1.4233e-04 lr: 1.4757e-05 eta: 2 days, 11:39:47 time: 2.8075 data_time: 0.1716 memory: 14136 grad_norm: 0.5990 loss: 0.5104 detection_loss_cls: 0.5104 2024/07/12 05:50:29 - mmengine - INFO - Iter(train) [ 43600/120000] base_lr: 1.4221e-04 lr: 1.4747e-05 eta: 2 days, 11:37:24 time: 2.8072 data_time: 0.1713 memory: 14136 grad_norm: 0.5989 loss: 0.5094 detection_loss_cls: 0.5094 2024/07/12 05:52:50 - mmengine - INFO - Iter(train) [ 43650/120000] base_lr: 1.4210e-04 lr: 1.4736e-05 eta: 2 days, 11:35:03 time: 2.8071 data_time: 0.1716 memory: 14136 grad_norm: 0.5983 loss: 0.5100 detection_loss_cls: 0.5100 2024/07/12 05:55:10 - mmengine - INFO - Iter(train) [ 43700/120000] base_lr: 1.4198e-04 lr: 1.4725e-05 eta: 2 days, 11:32:42 time: 2.8068 data_time: 0.1714 memory: 14136 grad_norm: 0.5983 loss: 0.5095 detection_loss_cls: 0.5095 2024/07/12 05:57:30 - mmengine - INFO - Iter(train) [ 43750/120000] base_lr: 1.4186e-04 lr: 1.4715e-05 eta: 2 days, 11:30:21 time: 2.8070 data_time: 0.1716 memory: 14136 grad_norm: 0.5982 loss: 0.5095 detection_loss_cls: 0.5095 2024/07/12 05:59:49 - mmengine - INFO - Iter(train) [ 43800/120000] base_lr: 1.4174e-04 lr: 1.4704e-05 eta: 2 days, 11:27:56 time: 2.8066 data_time: 0.1717 memory: 14136 grad_norm: 0.5982 loss: 0.5088 detection_loss_cls: 0.5088 2024/07/12 06:02:10 - mmengine - INFO - Iter(train) [ 43850/120000] base_lr: 1.4162e-04 lr: 1.4693e-05 eta: 2 days, 11:25:34 time: 2.8064 data_time: 0.1718 memory: 14136 grad_norm: 0.5983 loss: 0.5087 detection_loss_cls: 0.5087 2024/07/12 06:04:30 - mmengine - INFO - Iter(train) [ 43900/120000] base_lr: 1.4151e-04 lr: 1.4682e-05 eta: 2 days, 11:23:14 time: 2.8064 data_time: 0.1717 memory: 14136 grad_norm: 0.5982 loss: 0.5077 detection_loss_cls: 0.5077 2024/07/12 06:06:50 - mmengine - INFO - Iter(train) [ 43950/120000] base_lr: 1.4139e-04 lr: 1.4672e-05 eta: 2 days, 11:20:52 time: 2.8064 data_time: 0.1719 memory: 14136 grad_norm: 0.5986 loss: 0.5077 detection_loss_cls: 0.5077 2024/07/12 06:09:10 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 06:09:10 - mmengine - INFO - Iter(train) [ 44000/120000] base_lr: 1.4127e-04 lr: 1.4661e-05 eta: 2 days, 11:18:31 time: 2.8063 data_time: 0.1717 memory: 14136 grad_norm: 0.5983 loss: 0.5072 detection_loss_cls: 0.5072 2024/07/12 06:09:10 - mmengine - INFO - Saving checkpoint at 44000 iterations 2024/07/12 06:11:38 - mmengine - INFO - Iter(train) [ 44050/120000] base_lr: 1.4115e-04 lr: 1.4650e-05 eta: 2 days, 11:16:32 time: 2.8009 data_time: 0.1664 memory: 14135 grad_norm: 0.5982 loss: 0.5079 detection_loss_cls: 0.5079 2024/07/12 06:13:58 - mmengine - INFO - Iter(train) [ 44100/120000] base_lr: 1.4103e-04 lr: 1.4639e-05 eta: 2 days, 11:14:12 time: 2.8012 data_time: 0.1668 memory: 14136 grad_norm: 0.5982 loss: 0.5085 detection_loss_cls: 0.5085 2024/07/12 06:16:18 - mmengine - INFO - Iter(train) [ 44150/120000] base_lr: 1.4091e-04 lr: 1.4629e-05 eta: 2 days, 11:11:49 time: 2.8007 data_time: 0.1669 memory: 14136 grad_norm: 0.5983 loss: 0.5085 detection_loss_cls: 0.5085 2024/07/12 06:18:37 - mmengine - INFO - Iter(train) [ 44200/120000] base_lr: 1.4080e-04 lr: 1.4618e-05 eta: 2 days, 11:09:25 time: 2.8004 data_time: 0.1672 memory: 14136 grad_norm: 0.5983 loss: 0.5089 detection_loss_cls: 0.5089 2024/07/12 06:20:57 - mmengine - INFO - Iter(train) [ 44250/120000] base_lr: 1.4068e-04 lr: 1.4607e-05 eta: 2 days, 11:07:03 time: 2.8006 data_time: 0.1671 memory: 14136 grad_norm: 0.5984 loss: 0.5087 detection_loss_cls: 0.5087 2024/07/12 06:23:17 - mmengine - INFO - Iter(train) [ 44300/120000] base_lr: 1.4056e-04 lr: 1.4596e-05 eta: 2 days, 11:04:41 time: 2.8008 data_time: 0.1671 memory: 14135 grad_norm: 0.5982 loss: 0.5082 detection_loss_cls: 0.5082 2024/07/12 06:25:36 - mmengine - INFO - Iter(train) [ 44350/120000] base_lr: 1.4044e-04 lr: 1.4585e-05 eta: 2 days, 11:02:14 time: 2.8003 data_time: 0.1669 memory: 14136 grad_norm: 0.5975 loss: 0.5076 detection_loss_cls: 0.5076 2024/07/12 06:27:55 - mmengine - INFO - Iter(train) [ 44400/120000] base_lr: 1.4032e-04 lr: 1.4575e-05 eta: 2 days, 10:59:50 time: 2.8000 data_time: 0.1665 memory: 14135 grad_norm: 0.5977 loss: 0.5070 detection_loss_cls: 0.5070 2024/07/12 06:30:16 - mmengine - INFO - Iter(train) [ 44450/120000] base_lr: 1.4020e-04 lr: 1.4564e-05 eta: 2 days, 10:57:31 time: 2.8009 data_time: 0.1662 memory: 14136 grad_norm: 0.5979 loss: 0.5064 detection_loss_cls: 0.5064 2024/07/12 06:32:35 - mmengine - INFO - Iter(train) [ 44500/120000] base_lr: 1.4008e-04 lr: 1.4553e-05 eta: 2 days, 10:55:06 time: 2.8010 data_time: 0.1659 memory: 14136 grad_norm: 0.5979 loss: 0.5056 detection_loss_cls: 0.5056 2024/07/12 06:34:54 - mmengine - INFO - Iter(train) [ 44550/120000] base_lr: 1.3996e-04 lr: 1.4542e-05 eta: 2 days, 10:52:41 time: 2.8006 data_time: 0.1656 memory: 14136 grad_norm: 0.5980 loss: 0.5047 detection_loss_cls: 0.5047 2024/07/12 06:37:13 - mmengine - INFO - Iter(train) [ 44600/120000] base_lr: 1.3984e-04 lr: 1.4531e-05 eta: 2 days, 10:50:15 time: 2.8002 data_time: 0.1654 memory: 14136 grad_norm: 0.5980 loss: 0.5045 detection_loss_cls: 0.5045 2024/07/12 06:39:32 - mmengine - INFO - Iter(train) [ 44650/120000] base_lr: 1.3972e-04 lr: 1.4520e-05 eta: 2 days, 10:47:51 time: 2.7999 data_time: 0.1655 memory: 14136 grad_norm: 0.5981 loss: 0.5050 detection_loss_cls: 0.5050 2024/07/12 06:41:51 - mmengine - INFO - Iter(train) [ 44700/120000] base_lr: 1.3961e-04 lr: 1.4510e-05 eta: 2 days, 10:45:26 time: 2.7996 data_time: 0.1652 memory: 14136 grad_norm: 0.5980 loss: 0.5048 detection_loss_cls: 0.5048 2024/07/12 06:44:10 - mmengine - INFO - Iter(train) [ 44750/120000] base_lr: 1.3949e-04 lr: 1.4499e-05 eta: 2 days, 10:43:01 time: 2.7993 data_time: 0.1647 memory: 14135 grad_norm: 0.5975 loss: 0.5036 detection_loss_cls: 0.5036 2024/07/12 06:46:30 - mmengine - INFO - Iter(train) [ 44800/120000] base_lr: 1.3937e-04 lr: 1.4488e-05 eta: 2 days, 10:40:38 time: 2.7989 data_time: 0.1644 memory: 14136 grad_norm: 0.5975 loss: 0.5030 detection_loss_cls: 0.5030 2024/07/12 06:48:49 - mmengine - INFO - Iter(train) [ 44850/120000] base_lr: 1.3925e-04 lr: 1.4477e-05 eta: 2 days, 10:38:15 time: 2.7988 data_time: 0.1643 memory: 14136 grad_norm: 0.5974 loss: 0.5027 detection_loss_cls: 0.5027 2024/07/12 06:51:11 - mmengine - INFO - Iter(train) [ 44900/120000] base_lr: 1.3913e-04 lr: 1.4466e-05 eta: 2 days, 10:35:57 time: 2.7995 data_time: 0.1647 memory: 14136 grad_norm: 0.5974 loss: 0.5036 detection_loss_cls: 0.5036 2024/07/12 06:53:31 - mmengine - INFO - Iter(train) [ 44950/120000] base_lr: 1.3901e-04 lr: 1.4455e-05 eta: 2 days, 10:33:35 time: 2.7995 data_time: 0.1649 memory: 14136 grad_norm: 0.5976 loss: 0.5040 detection_loss_cls: 0.5040 2024/07/12 06:55:51 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 06:55:51 - mmengine - INFO - Iter(train) [ 45000/120000] base_lr: 1.3889e-04 lr: 1.4444e-05 eta: 2 days, 10:31:16 time: 2.7999 data_time: 0.1651 memory: 14136 grad_norm: 0.5976 loss: 0.5039 detection_loss_cls: 0.5039 2024/07/12 06:55:51 - mmengine - INFO - Saving checkpoint at 45000 iterations 2024/07/12 06:56:20 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:23 time: 0.4130 data_time: 0.0048 memory: 5703 2024/07/12 06:56:41 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:02 time: 0.4128 data_time: 0.0048 memory: 5703 2024/07/12 06:57:01 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:42 time: 0.4128 data_time: 0.0048 memory: 5703 2024/07/12 06:57:22 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:21 time: 0.4127 data_time: 0.0048 memory: 5703 2024/07/12 06:57:43 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:01 time: 0.4125 data_time: 0.0048 memory: 5703 2024/07/12 06:58:03 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:40 time: 0.4124 data_time: 0.0048 memory: 5703 2024/07/12 06:58:24 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:19 time: 0.4123 data_time: 0.0048 memory: 5703 2024/07/12 06:58:44 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:59 time: 0.4122 data_time: 0.0048 memory: 5703 2024/07/12 06:59:05 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:38 time: 0.4121 data_time: 0.0048 memory: 5703 2024/07/12 06:59:25 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:17 time: 0.4119 data_time: 0.0047 memory: 5703 2024/07/12 06:59:46 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4117 data_time: 0.0047 memory: 5703 2024/07/12 07:00:06 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:36 time: 0.4117 data_time: 0.0047 memory: 5703 2024/07/12 07:00:27 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4115 data_time: 0.0047 memory: 5703 2024/07/12 07:00:48 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:55 time: 0.4115 data_time: 0.0047 memory: 5703 2024/07/12 07:01:08 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4115 data_time: 0.0047 memory: 5703 2024/07/12 07:01:29 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:14 time: 0.4116 data_time: 0.0047 memory: 5703 2024/07/12 07:01:45 - mmengine - INFO - Evaluating bbox... 2024/07/12 07:02:11 - mmengine - INFO - bbox_mAP_copypaste: 0.438 0.602 0.477 0.254 0.475 0.589 2024/07/12 07:02:11 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4380 coco/bbox_mAP_50: 0.6020 coco/bbox_mAP_75: 0.4770 coco/bbox_mAP_s: 0.2540 coco/bbox_mAP_m: 0.4750 coco/bbox_mAP_l: 0.5890 data_time: 0.0047 time: 0.4117 2024/07/12 07:04:30 - mmengine - INFO - Iter(train) [ 45050/120000] base_lr: 1.3877e-04 lr: 1.4433e-05 eta: 2 days, 10:30:17 time: 2.8048 data_time: 0.1702 memory: 14136 grad_norm: 0.5977 loss: 0.5044 detection_loss_cls: 0.5044 2024/07/12 07:06:50 - 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mmengine - INFO - Saving checkpoint at 46000 iterations 2024/07/12 07:51:18 - mmengine - INFO - Iter(train) [ 46050/120000] base_lr: 1.3636e-04 lr: 1.4215e-05 eta: 2 days, 9:43:20 time: 2.8072 data_time: 0.1695 memory: 14135 grad_norm: 0.5991 loss: 0.5063 detection_loss_cls: 0.5063 2024/07/12 07:53:39 - mmengine - INFO - Iter(train) [ 46100/120000] base_lr: 1.3624e-04 lr: 1.4204e-05 eta: 2 days, 9:40:58 time: 2.8074 data_time: 0.1694 memory: 14136 grad_norm: 0.5993 loss: 0.5056 detection_loss_cls: 0.5056 2024/07/12 07:56:00 - mmengine - INFO - Iter(train) [ 46150/120000] base_lr: 1.3612e-04 lr: 1.4193e-05 eta: 2 days, 9:38:39 time: 2.8078 data_time: 0.1696 memory: 14136 grad_norm: 0.5993 loss: 0.5060 detection_loss_cls: 0.5060 2024/07/12 07:58:19 - mmengine - INFO - Iter(train) [ 46200/120000] base_lr: 1.3600e-04 lr: 1.4181e-05 eta: 2 days, 9:36:15 time: 2.8075 data_time: 0.1698 memory: 14136 grad_norm: 0.5997 loss: 0.5066 detection_loss_cls: 0.5066 2024/07/12 08:00:38 - mmengine - INFO - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 08:35:30 - mmengine - INFO - Iter(train) [ 47000/120000] base_lr: 1.3405e-04 lr: 1.4004e-05 eta: 2 days, 8:58:00 time: 2.8071 data_time: 0.1678 memory: 14136 grad_norm: 0.5996 loss: 0.5048 detection_loss_cls: 0.5048 2024/07/12 08:35:30 - mmengine - INFO - Saving checkpoint at 47000 iterations 2024/07/12 08:37:56 - mmengine - INFO - Iter(train) [ 47050/120000] base_lr: 1.3393e-04 lr: 1.3993e-05 eta: 2 days, 8:55:57 time: 2.8069 data_time: 0.1679 memory: 14136 grad_norm: 0.5997 loss: 0.5043 detection_loss_cls: 0.5043 2024/07/12 08:40:16 - mmengine - INFO - Iter(train) [ 47100/120000] base_lr: 1.3380e-04 lr: 1.3982e-05 eta: 2 days, 8:53:33 time: 2.8071 data_time: 0.1678 memory: 14136 grad_norm: 0.5999 loss: 0.5042 detection_loss_cls: 0.5042 2024/07/12 08:42:35 - mmengine - INFO - Iter(train) [ 47150/120000] base_lr: 1.3368e-04 lr: 1.3971e-05 eta: 2 days, 8:51:10 time: 2.8070 data_time: 0.1678 memory: 14135 grad_norm: 0.5998 loss: 0.5041 detection_loss_cls: 0.5041 2024/07/12 08:44:56 - 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mmengine - INFO - Iter(train) [ 47950/120000] base_lr: 1.3172e-04 lr: 1.3793e-05 eta: 2 days, 8:13:18 time: 2.8078 data_time: 0.1671 memory: 14136 grad_norm: 0.5996 loss: 0.5059 detection_loss_cls: 0.5059 2024/07/12 09:22:14 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 09:22:14 - mmengine - INFO - Iter(train) [ 48000/120000] base_lr: 1.3160e-04 lr: 1.3781e-05 eta: 2 days, 8:10:57 time: 2.8078 data_time: 0.1667 memory: 14136 grad_norm: 0.6000 loss: 0.5054 detection_loss_cls: 0.5054 2024/07/12 09:22:14 - mmengine - INFO - Saving checkpoint at 48000 iterations 2024/07/12 09:24:42 - mmengine - INFO - Iter(train) [ 48050/120000] base_lr: 1.3147e-04 lr: 1.3770e-05 eta: 2 days, 8:08:55 time: 2.8079 data_time: 0.1663 memory: 14136 grad_norm: 0.6003 loss: 0.5043 detection_loss_cls: 0.5043 2024/07/12 09:27:02 - mmengine - INFO - Iter(train) [ 48100/120000] base_lr: 1.3135e-04 lr: 1.3759e-05 eta: 2 days, 8:06:34 time: 2.8078 data_time: 0.1657 memory: 14136 grad_norm: 0.6005 loss: 0.5037 detection_loss_cls: 0.5037 2024/07/12 09:29:21 - 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mmengine - INFO - Iter(train) [ 48900/120000] base_lr: 1.2937e-04 lr: 1.3579e-05 eta: 2 days, 7:28:44 time: 2.8093 data_time: 0.1661 memory: 14136 grad_norm: 0.6016 loss: 0.5042 detection_loss_cls: 0.5042 2024/07/12 10:06:40 - mmengine - INFO - Iter(train) [ 48950/120000] base_lr: 1.2924e-04 lr: 1.3568e-05 eta: 2 days, 7:26:21 time: 2.8092 data_time: 0.1658 memory: 14136 grad_norm: 0.6018 loss: 0.5036 detection_loss_cls: 0.5036 2024/07/12 10:09:00 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 10:09:00 - mmengine - INFO - Iter(train) [ 49000/120000] base_lr: 1.2912e-04 lr: 1.3556e-05 eta: 2 days, 7:24:00 time: 2.8090 data_time: 0.1658 memory: 14136 grad_norm: 0.6018 loss: 0.5035 detection_loss_cls: 0.5035 2024/07/12 10:09:00 - mmengine - INFO - Saving checkpoint at 49000 iterations 2024/07/12 10:11:29 - mmengine - INFO - Iter(train) [ 49050/120000] base_lr: 1.2900e-04 lr: 1.3545e-05 eta: 2 days, 7:22:00 time: 2.8044 data_time: 0.1605 memory: 14136 grad_norm: 0.6019 loss: 0.5031 detection_loss_cls: 0.5031 2024/07/12 10:13:49 - mmengine - INFO - Iter(train) [ 49100/120000] base_lr: 1.2887e-04 lr: 1.3534e-05 eta: 2 days, 7:19:39 time: 2.8044 data_time: 0.1603 memory: 14135 grad_norm: 0.6022 loss: 0.5026 detection_loss_cls: 0.5026 2024/07/12 10:16:09 - mmengine - INFO - Iter(train) [ 49150/120000] base_lr: 1.2875e-04 lr: 1.3522e-05 eta: 2 days, 7:17:18 time: 2.8043 data_time: 0.1602 memory: 14136 grad_norm: 0.6023 loss: 0.5025 detection_loss_cls: 0.5025 2024/07/12 10:18:29 - mmengine - INFO - Iter(train) [ 49200/120000] base_lr: 1.2862e-04 lr: 1.3511e-05 eta: 2 days, 7:14:55 time: 2.8044 data_time: 0.1604 memory: 14136 grad_norm: 0.6024 loss: 0.5030 detection_loss_cls: 0.5030 2024/07/12 10:20:48 - mmengine - INFO - Iter(train) [ 49250/120000] base_lr: 1.2850e-04 lr: 1.3500e-05 eta: 2 days, 7:12:31 time: 2.8042 data_time: 0.1604 memory: 14136 grad_norm: 0.6018 loss: 0.5026 detection_loss_cls: 0.5026 2024/07/12 10:23:08 - mmengine - INFO - Iter(train) [ 49300/120000] base_lr: 1.2837e-04 lr: 1.3489e-05 eta: 2 days, 7:10:10 time: 2.8043 data_time: 0.1604 memory: 14136 grad_norm: 0.6019 loss: 0.5022 detection_loss_cls: 0.5022 2024/07/12 10:25:28 - mmengine - INFO - Iter(train) [ 49350/120000] base_lr: 1.2825e-04 lr: 1.3477e-05 eta: 2 days, 7:07:49 time: 2.8043 data_time: 0.1606 memory: 14136 grad_norm: 0.6018 loss: 0.5028 detection_loss_cls: 0.5028 2024/07/12 10:27:49 - mmengine - INFO - Iter(train) [ 49400/120000] base_lr: 1.2812e-04 lr: 1.3466e-05 eta: 2 days, 7:05:29 time: 2.8043 data_time: 0.1606 memory: 14136 grad_norm: 0.6019 loss: 0.5025 detection_loss_cls: 0.5025 2024/07/12 10:30:10 - mmengine - INFO - Iter(train) [ 49450/120000] base_lr: 1.2800e-04 lr: 1.3455e-05 eta: 2 days, 7:03:10 time: 2.8046 data_time: 0.1605 memory: 14136 grad_norm: 0.6016 loss: 0.5017 detection_loss_cls: 0.5017 2024/07/12 10:32:29 - mmengine - INFO - Iter(train) [ 49500/120000] base_lr: 1.2788e-04 lr: 1.3443e-05 eta: 2 days, 7:00:47 time: 2.8045 data_time: 0.1608 memory: 14136 grad_norm: 0.6015 loss: 0.5016 detection_loss_cls: 0.5016 2024/07/12 10:34:50 - mmengine - INFO - Iter(train) [ 49550/120000] base_lr: 1.2775e-04 lr: 1.3432e-05 eta: 2 days, 6:58:27 time: 2.8044 data_time: 0.1607 memory: 14136 grad_norm: 0.6013 loss: 0.5011 detection_loss_cls: 0.5011 2024/07/12 10:37:10 - mmengine - INFO - Iter(train) [ 49600/120000] base_lr: 1.2763e-04 lr: 1.3421e-05 eta: 2 days, 6:56:05 time: 2.8040 data_time: 0.1608 memory: 14136 grad_norm: 0.6009 loss: 0.5007 detection_loss_cls: 0.5007 2024/07/12 10:39:28 - mmengine - INFO - Iter(train) [ 49650/120000] base_lr: 1.2750e-04 lr: 1.3409e-05 eta: 2 days, 6:53:40 time: 2.8035 data_time: 0.1611 memory: 14136 grad_norm: 0.6011 loss: 0.5012 detection_loss_cls: 0.5012 2024/07/12 10:41:50 - mmengine - INFO - Iter(train) [ 49700/120000] base_lr: 1.2738e-04 lr: 1.3398e-05 eta: 2 days, 6:51:22 time: 2.8038 data_time: 0.1611 memory: 14136 grad_norm: 0.6011 loss: 0.5008 detection_loss_cls: 0.5008 2024/07/12 10:44:09 - mmengine - INFO - Iter(train) [ 49750/120000] base_lr: 1.2725e-04 lr: 1.3386e-05 eta: 2 days, 6:48:59 time: 2.8041 data_time: 0.1609 memory: 14136 grad_norm: 0.6012 loss: 0.4999 detection_loss_cls: 0.4999 2024/07/12 10:46:30 - mmengine - INFO - Iter(train) [ 49800/120000] base_lr: 1.2713e-04 lr: 1.3375e-05 eta: 2 days, 6:46:40 time: 2.8041 data_time: 0.1611 memory: 14136 grad_norm: 0.6012 loss: 0.5000 detection_loss_cls: 0.5000 2024/07/12 10:48:50 - mmengine - INFO - Iter(train) [ 49850/120000] base_lr: 1.2700e-04 lr: 1.3364e-05 eta: 2 days, 6:44:18 time: 2.8041 data_time: 0.1608 memory: 14136 grad_norm: 0.6013 loss: 0.4995 detection_loss_cls: 0.4995 2024/07/12 10:51:11 - mmengine - INFO - Iter(train) [ 49900/120000] base_lr: 1.2688e-04 lr: 1.3352e-05 eta: 2 days, 6:41:58 time: 2.8044 data_time: 0.1610 memory: 14136 grad_norm: 0.6014 loss: 0.4997 detection_loss_cls: 0.4997 2024/07/12 10:53:31 - mmengine - INFO - Iter(train) [ 49950/120000] base_lr: 1.2675e-04 lr: 1.3341e-05 eta: 2 days, 6:39:37 time: 2.8044 data_time: 0.1610 memory: 14136 grad_norm: 0.6011 loss: 0.4995 detection_loss_cls: 0.4995 2024/07/12 10:55:50 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 10:55:50 - mmengine - INFO - Iter(train) [ 50000/120000] base_lr: 1.2663e-04 lr: 1.3330e-05 eta: 2 days, 6:37:14 time: 2.8046 data_time: 0.1608 memory: 14136 grad_norm: 0.6016 loss: 0.4985 detection_loss_cls: 0.4985 2024/07/12 10:55:50 - mmengine - INFO - Saving checkpoint at 50000 iterations 2024/07/12 10:56:19 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:26 time: 0.4117 data_time: 0.0047 memory: 5703 2024/07/12 10:56:40 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:04 time: 0.4118 data_time: 0.0047 memory: 5703 2024/07/12 10:57:00 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:44 time: 0.4119 data_time: 0.0047 memory: 5703 2024/07/12 10:57:21 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:23 time: 0.4120 data_time: 0.0047 memory: 5703 2024/07/12 10:57:42 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:02 time: 0.4121 data_time: 0.0047 memory: 5703 2024/07/12 10:58:03 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:41 time: 0.4121 data_time: 0.0047 memory: 5703 2024/07/12 10:58:23 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:20 time: 0.4122 data_time: 0.0047 memory: 5703 2024/07/12 10:58:44 - mmengine - INFO - Iter(val) [400/834] eta: 0:03:00 time: 0.4123 data_time: 0.0047 memory: 5703 2024/07/12 10:59:05 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:39 time: 0.4123 data_time: 0.0047 memory: 5703 2024/07/12 10:59:25 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:18 time: 0.4123 data_time: 0.0047 memory: 5703 2024/07/12 10:59:46 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:57 time: 0.4124 data_time: 0.0047 memory: 5703 2024/07/12 11:00:07 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:36 time: 0.4125 data_time: 0.0047 memory: 5703 2024/07/12 11:00:28 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:16 time: 0.4126 data_time: 0.0047 memory: 5703 2024/07/12 11:00:48 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:55 time: 0.4126 data_time: 0.0047 memory: 5703 2024/07/12 11:01:09 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4126 data_time: 0.0047 memory: 5703 2024/07/12 11:01:30 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:14 time: 0.4127 data_time: 0.0047 memory: 5703 2024/07/12 11:01:46 - mmengine - INFO - Evaluating bbox... 2024/07/12 11:02:11 - mmengine - INFO - bbox_mAP_copypaste: 0.444 0.609 0.482 0.259 0.481 0.598 2024/07/12 11:02:12 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4440 coco/bbox_mAP_50: 0.6090 coco/bbox_mAP_75: 0.4820 coco/bbox_mAP_s: 0.2590 coco/bbox_mAP_m: 0.4810 coco/bbox_mAP_l: 0.5980 data_time: 0.0048 time: 0.4145 2024/07/12 11:04:32 - mmengine - INFO - Iter(train) [ 50050/120000] base_lr: 1.2650e-04 lr: 1.3318e-05 eta: 2 days, 6:35:58 time: 2.8098 data_time: 0.1656 memory: 14136 grad_norm: 0.6019 loss: 0.4981 detection_loss_cls: 0.4981 2024/07/12 11:06:53 - mmengine - INFO - Iter(train) [ 50100/120000] base_lr: 1.2638e-04 lr: 1.3307e-05 eta: 2 days, 6:33:38 time: 2.8100 data_time: 0.1657 memory: 14136 grad_norm: 0.6017 loss: 0.4985 detection_loss_cls: 0.4985 2024/07/12 11:09:12 - mmengine - INFO - Iter(train) [ 50150/120000] base_lr: 1.2625e-04 lr: 1.3295e-05 eta: 2 days, 6:31:15 time: 2.8096 data_time: 0.1656 memory: 14136 grad_norm: 0.6017 loss: 0.4983 detection_loss_cls: 0.4983 2024/07/12 11:11:32 - mmengine - INFO - Iter(train) [ 50200/120000] base_lr: 1.2612e-04 lr: 1.3284e-05 eta: 2 days, 6:28:52 time: 2.8097 data_time: 0.1651 memory: 14136 grad_norm: 0.6015 loss: 0.4969 detection_loss_cls: 0.4969 2024/07/12 11:13:52 - mmengine - INFO - Iter(train) [ 50250/120000] base_lr: 1.2600e-04 lr: 1.3273e-05 eta: 2 days, 6:26:31 time: 2.8099 data_time: 0.1649 memory: 14136 grad_norm: 0.6015 loss: 0.4964 detection_loss_cls: 0.4964 2024/07/12 11:16:13 - mmengine - INFO - Iter(train) [ 50300/120000] base_lr: 1.2587e-04 lr: 1.3261e-05 eta: 2 days, 6:24:12 time: 2.8102 data_time: 0.1649 memory: 14136 grad_norm: 0.6018 loss: 0.4969 detection_loss_cls: 0.4969 2024/07/12 11:18:33 - mmengine - INFO - Iter(train) [ 50350/120000] base_lr: 1.2575e-04 lr: 1.3250e-05 eta: 2 days, 6:21:51 time: 2.8104 data_time: 0.1647 memory: 14136 grad_norm: 0.6019 loss: 0.4966 detection_loss_cls: 0.4966 2024/07/12 11:20:55 - mmengine - INFO - Iter(train) [ 50400/120000] base_lr: 1.2562e-04 lr: 1.3238e-05 eta: 2 days, 6:19:34 time: 2.8109 data_time: 0.1648 memory: 14136 grad_norm: 0.6016 loss: 0.4965 detection_loss_cls: 0.4965 2024/07/12 11:23:16 - mmengine - INFO - Iter(train) [ 50450/120000] base_lr: 1.2550e-04 lr: 1.3227e-05 eta: 2 days, 6:17:13 time: 2.8110 data_time: 0.1649 memory: 14136 grad_norm: 0.6016 loss: 0.4967 detection_loss_cls: 0.4967 2024/07/12 11:25:35 - mmengine - INFO - Iter(train) [ 50500/120000] base_lr: 1.2537e-04 lr: 1.3216e-05 eta: 2 days, 6:14:50 time: 2.8112 data_time: 0.1653 memory: 14136 grad_norm: 0.6017 loss: 0.4967 detection_loss_cls: 0.4967 2024/07/12 11:27:55 - mmengine - INFO - Iter(train) [ 50550/120000] base_lr: 1.2525e-04 lr: 1.3204e-05 eta: 2 days, 6:12:29 time: 2.8114 data_time: 0.1654 memory: 14136 grad_norm: 0.6019 loss: 0.4966 detection_loss_cls: 0.4966 2024/07/12 11:30:16 - mmengine - INFO - Iter(train) [ 50600/120000] base_lr: 1.2512e-04 lr: 1.3193e-05 eta: 2 days, 6:10:08 time: 2.8118 data_time: 0.1654 memory: 14136 grad_norm: 0.6018 loss: 0.4966 detection_loss_cls: 0.4966 2024/07/12 11:32:36 - mmengine - INFO - Iter(train) [ 50650/120000] base_lr: 1.2499e-04 lr: 1.3181e-05 eta: 2 days, 6:07:48 time: 2.8121 data_time: 0.1653 memory: 14136 grad_norm: 0.6017 loss: 0.4965 detection_loss_cls: 0.4965 2024/07/12 11:34:56 - mmengine - INFO - Iter(train) [ 50700/120000] base_lr: 1.2487e-04 lr: 1.3170e-05 eta: 2 days, 6:05:25 time: 2.8121 data_time: 0.1654 memory: 14136 grad_norm: 0.6020 loss: 0.4966 detection_loss_cls: 0.4966 2024/07/12 11:37:17 - mmengine - INFO - Iter(train) [ 50750/120000] base_lr: 1.2474e-04 lr: 1.3158e-05 eta: 2 days, 6:03:06 time: 2.8124 data_time: 0.1652 memory: 14136 grad_norm: 0.6019 loss: 0.4961 detection_loss_cls: 0.4961 2024/07/12 11:39:39 - mmengine - INFO - Iter(train) [ 50800/120000] base_lr: 1.2462e-04 lr: 1.3147e-05 eta: 2 days, 6:00:48 time: 2.8129 data_time: 0.1655 memory: 14136 grad_norm: 0.6023 loss: 0.4969 detection_loss_cls: 0.4969 2024/07/12 11:41:59 - mmengine - INFO - Iter(train) [ 50850/120000] base_lr: 1.2449e-04 lr: 1.3136e-05 eta: 2 days, 5:58:27 time: 2.8133 data_time: 0.1659 memory: 14135 grad_norm: 0.6024 loss: 0.4972 detection_loss_cls: 0.4972 2024/07/12 11:44:19 - mmengine - INFO - Iter(train) [ 50900/120000] base_lr: 1.2437e-04 lr: 1.3124e-05 eta: 2 days, 5:56:05 time: 2.8134 data_time: 0.1656 memory: 14136 grad_norm: 0.6022 loss: 0.4963 detection_loss_cls: 0.4963 2024/07/12 11:46:38 - mmengine - INFO - Iter(train) [ 50950/120000] base_lr: 1.2424e-04 lr: 1.3113e-05 eta: 2 days, 5:53:43 time: 2.8135 data_time: 0.1656 memory: 14136 grad_norm: 0.6024 loss: 0.4954 detection_loss_cls: 0.4954 2024/07/12 11:48:58 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 11:48:58 - mmengine - INFO - Iter(train) [ 51000/120000] base_lr: 1.2411e-04 lr: 1.3101e-05 eta: 2 days, 5:51:20 time: 2.8135 data_time: 0.1657 memory: 14136 grad_norm: 0.6023 loss: 0.4959 detection_loss_cls: 0.4959 2024/07/12 11:48:58 - mmengine - INFO - Saving checkpoint at 51000 iterations 2024/07/12 11:51:27 - mmengine - INFO - Iter(train) [ 51050/120000] base_lr: 1.2399e-04 lr: 1.3090e-05 eta: 2 days, 5:49:17 time: 2.8139 data_time: 0.1658 memory: 14136 grad_norm: 0.6022 loss: 0.4960 detection_loss_cls: 0.4960 2024/07/12 11:53:47 - mmengine - INFO - Iter(train) [ 51100/120000] base_lr: 1.2386e-04 lr: 1.3078e-05 eta: 2 days, 5:46:57 time: 2.8142 data_time: 0.1659 memory: 14136 grad_norm: 0.6021 loss: 0.4963 detection_loss_cls: 0.4963 2024/07/12 11:56:07 - 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mmengine - INFO - Saving checkpoint at 53000 iterations 2024/07/12 13:25:12 - mmengine - INFO - Iter(train) [ 53050/120000] base_lr: 1.1892e-04 lr: 1.2629e-05 eta: 2 days, 4:15:47 time: 2.8171 data_time: 0.1671 memory: 14136 grad_norm: 0.6007 loss: 0.4936 detection_loss_cls: 0.4936 2024/07/12 13:27:32 - mmengine - INFO - Iter(train) [ 53100/120000] base_lr: 1.1879e-04 lr: 1.2617e-05 eta: 2 days, 4:13:25 time: 2.8171 data_time: 0.1676 memory: 14136 grad_norm: 0.6006 loss: 0.4945 detection_loss_cls: 0.4945 2024/07/12 13:29:52 - mmengine - INFO - Iter(train) [ 53150/120000] base_lr: 1.1866e-04 lr: 1.2606e-05 eta: 2 days, 4:11:04 time: 2.8172 data_time: 0.1680 memory: 14136 grad_norm: 0.6005 loss: 0.4946 detection_loss_cls: 0.4946 2024/07/12 13:32:13 - mmengine - INFO - Iter(train) [ 53200/120000] base_lr: 1.1853e-04 lr: 1.2594e-05 eta: 2 days, 4:08:43 time: 2.8174 data_time: 0.1680 memory: 14136 grad_norm: 0.6005 loss: 0.4944 detection_loss_cls: 0.4944 2024/07/12 13:34:34 - mmengine - INFO - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 14:09:34 - mmengine - INFO - Iter(train) [ 54000/120000] base_lr: 1.1649e-04 lr: 1.2408e-05 eta: 2 days, 3:31:01 time: 2.8172 data_time: 0.1676 memory: 14136 grad_norm: 0.6019 loss: 0.4933 detection_loss_cls: 0.4933 2024/07/12 14:09:34 - mmengine - INFO - Saving checkpoint at 54000 iterations 2024/07/12 14:12:01 - mmengine - INFO - Iter(train) [ 54050/120000] base_lr: 1.1636e-04 lr: 1.2397e-05 eta: 2 days, 3:28:54 time: 2.8120 data_time: 0.1627 memory: 14136 grad_norm: 0.6017 loss: 0.4936 detection_loss_cls: 0.4936 2024/07/12 14:14:20 - mmengine - INFO - Iter(train) [ 54100/120000] base_lr: 1.1623e-04 lr: 1.2385e-05 eta: 2 days, 3:26:30 time: 2.8115 data_time: 0.1624 memory: 14136 grad_norm: 0.6022 loss: 0.4934 detection_loss_cls: 0.4934 2024/07/12 14:16:40 - mmengine - INFO - Iter(train) [ 54150/120000] base_lr: 1.1611e-04 lr: 1.2373e-05 eta: 2 days, 3:24:09 time: 2.8117 data_time: 0.1624 memory: 14136 grad_norm: 0.6022 loss: 0.4935 detection_loss_cls: 0.4935 2024/07/12 14:19:00 - 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mmengine - INFO - Iter(train) [ 54700/120000] base_lr: 1.1470e-04 lr: 1.2245e-05 eta: 2 days, 2:58:11 time: 2.8106 data_time: 0.1634 memory: 14136 grad_norm: 0.6021 loss: 0.4957 detection_loss_cls: 0.4957 2024/07/12 14:44:39 - mmengine - INFO - Iter(train) [ 54750/120000] base_lr: 1.1457e-04 lr: 1.2233e-05 eta: 2 days, 2:55:48 time: 2.8101 data_time: 0.1638 memory: 14136 grad_norm: 0.6021 loss: 0.4963 detection_loss_cls: 0.4963 2024/07/12 14:46:58 - mmengine - INFO - Iter(train) [ 54800/120000] base_lr: 1.1444e-04 lr: 1.2222e-05 eta: 2 days, 2:53:26 time: 2.8096 data_time: 0.1635 memory: 14136 grad_norm: 0.6016 loss: 0.4951 detection_loss_cls: 0.4951 2024/07/12 14:49:19 - mmengine - INFO - Iter(train) [ 54850/120000] base_lr: 1.1431e-04 lr: 1.2210e-05 eta: 2 days, 2:51:05 time: 2.8097 data_time: 0.1634 memory: 14136 grad_norm: 0.6014 loss: 0.4946 detection_loss_cls: 0.4946 2024/07/12 14:51:39 - mmengine - INFO - Iter(train) [ 54900/120000] base_lr: 1.1418e-04 lr: 1.2198e-05 eta: 2 days, 2:48:45 time: 2.8099 data_time: 0.1637 memory: 14136 grad_norm: 0.6014 loss: 0.4955 detection_loss_cls: 0.4955 2024/07/12 14:54:00 - mmengine - INFO - Iter(train) [ 54950/120000] base_lr: 1.1405e-04 lr: 1.2187e-05 eta: 2 days, 2:46:26 time: 2.8102 data_time: 0.1637 memory: 14135 grad_norm: 0.6013 loss: 0.4957 detection_loss_cls: 0.4957 2024/07/12 14:56:19 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240711_105932 2024/07/12 14:56:19 - mmengine - INFO - Iter(train) [ 55000/120000] base_lr: 1.1392e-04 lr: 1.2175e-05 eta: 2 days, 2:44:02 time: 2.8100 data_time: 0.1633 memory: 14136 grad_norm: 0.6014 loss: 0.4946 detection_loss_cls: 0.4946 2024/07/12 14:56:19 - mmengine - INFO - Saving checkpoint at 55000 iterations 2024/07/12 14:56:48 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:29 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 14:57:08 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:05 time: 0.4127 data_time: 0.0047 memory: 5703 2024/07/12 14:57:29 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:43 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 14:57:50 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:22 time: 0.4127 data_time: 0.0047 memory: 5703 2024/07/12 14:58:10 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:01 time: 0.4127 data_time: 0.0047 memory: 5703 2024/07/12 14:58:31 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:40 time: 0.4127 data_time: 0.0047 memory: 5703 2024/07/12 14:58:51 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:19 time: 0.4127 data_time: 0.0047 memory: 5703 2024/07/12 14:59:12 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:59 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 14:59:33 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:38 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 14:59:53 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:17 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 15:00:13 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:57 time: 0.4127 data_time: 0.0047 memory: 5703 2024/07/12 15:00:34 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:36 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 15:00:55 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 15:01:16 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:55 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 15:01:36 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 15:01:57 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:14 time: 0.4128 data_time: 0.0047 memory: 5703 2024/07/12 15:02:13 - mmengine - INFO - Evaluating bbox... 2024/07/12 15:02:39 - mmengine - INFO - bbox_mAP_copypaste: 0.448 0.615 0.486 0.257 0.490 0.601 2024/07/12 15:02:39 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4480 coco/bbox_mAP_50: 0.6150 coco/bbox_mAP_75: 0.4860 coco/bbox_mAP_s: 0.2570 coco/bbox_mAP_m: 0.4900 coco/bbox_mAP_l: 0.6010 data_time: 0.0047 time: 0.4123 2024/07/12 15:49:31 - mmengine - INFO - Iter(train) [ 55050/120000] base_lr: 1.1380e-04 lr: 1.2163e-05 eta: 2 days, 4:19:31 time: 2.8091 data_time: 0.1612 memory: 14085 grad_norm: 0.6014 loss: 0.4944 detection_loss_cls: 0.4944 2024/07/12 15:51:53 - mmengine - INFO - Iter(train) [ 55100/120000] base_lr: 1.1367e-04 lr: 1.2152e-05 eta: 2 days, 3:35:01 time: 2.8093 data_time: 0.1610 memory: 14084 grad_norm: 0.6014 loss: 0.4937 detection_loss_cls: 0.4937 2024/07/12 15:54:15 - mmengine - INFO - Iter(train) [ 55150/120000] base_lr: 1.1354e-04 lr: 1.2140e-05 eta: 2 days, 3:26:15 time: 2.8098 data_time: 0.1610 memory: 14083 grad_norm: 0.6013 loss: 0.4939 detection_loss_cls: 0.4939 2024/07/12 15:56:36 - mmengine - INFO - Iter(train) [ 55200/120000] base_lr: 1.1341e-04 lr: 1.2128e-05 eta: 2 days, 3:16:18 time: 2.8099 data_time: 0.1608 memory: 14083 grad_norm: 0.6016 loss: 0.4938 detection_loss_cls: 0.4938 2024/07/12 15:58:57 - mmengine - INFO - Iter(train) [ 55250/120000] base_lr: 1.1328e-04 lr: 1.2117e-05 eta: 2 days, 3:08:49 time: 2.8101 data_time: 0.1606 memory: 14084 grad_norm: 0.6018 loss: 0.4937 detection_loss_cls: 0.4937 2024/07/12 16:01:19 - mmengine - INFO - Iter(train) [ 55300/120000] base_lr: 1.1315e-04 lr: 1.2105e-05 eta: 2 days, 3:04:12 time: 2.8105 data_time: 0.1607 memory: 14084 grad_norm: 0.6017 loss: 0.4942 detection_loss_cls: 0.4942 2024/07/12 16:03:39 - mmengine - INFO - Iter(train) [ 55350/120000] base_lr: 1.1302e-04 lr: 1.2093e-05 eta: 2 days, 2:56:04 time: 2.8104 data_time: 0.1603 memory: 14084 grad_norm: 0.6017 loss: 0.4933 detection_loss_cls: 0.4933 2024/07/12 16:06:02 - mmengine - INFO - Iter(train) [ 55400/120000] base_lr: 1.1290e-04 lr: 1.2081e-05 eta: 2 days, 2:56:03 time: 2.8110 data_time: 0.1604 memory: 14084 grad_norm: 0.6017 loss: 0.4935 detection_loss_cls: 0.4935 2024/07/12 16:08:23 - mmengine - INFO - Iter(train) [ 55450/120000] base_lr: 1.1277e-04 lr: 1.2070e-05 eta: 2 days, 2:52:22 time: 2.8113 data_time: 0.1604 memory: 14084 grad_norm: 0.6022 loss: 0.4939 detection_loss_cls: 0.4939 2024/07/12 16:10:44 - mmengine - INFO - Iter(train) [ 55500/120000] base_lr: 1.1264e-04 lr: 1.2058e-05 eta: 2 days, 2:47:05 time: 2.8114 data_time: 0.1600 memory: 14084 grad_norm: 0.6020 loss: 0.4938 detection_loss_cls: 0.4938 2024/07/12 16:13:05 - mmengine - INFO - Iter(train) [ 55550/120000] base_lr: 1.1251e-04 lr: 1.2046e-05 eta: 2 days, 2:43:09 time: 2.8118 data_time: 0.1597 memory: 14084 grad_norm: 0.6026 loss: 0.4935 detection_loss_cls: 0.4935 2024/07/12 16:15:26 - mmengine - INFO - Iter(train) [ 55600/120000] base_lr: 1.1238e-04 lr: 1.2035e-05 eta: 2 days, 2:39:15 time: 2.8119 data_time: 0.1595 memory: 14084 grad_norm: 0.6025 loss: 0.4935 detection_loss_cls: 0.4935 2024/07/12 16:17:47 - mmengine - INFO - Iter(train) [ 55650/120000] base_lr: 1.1225e-04 lr: 1.2023e-05 eta: 2 days, 2:35:53 time: 2.8118 data_time: 0.1591 memory: 14084 grad_norm: 0.6027 loss: 0.4929 detection_loss_cls: 0.4929 2024/07/12 16:20:06 - mmengine - INFO - Iter(train) [ 55700/120000] base_lr: 1.1212e-04 lr: 1.2011e-05 eta: 2 days, 2:30:27 time: 2.8115 data_time: 0.1590 memory: 14084 grad_norm: 0.6028 loss: 0.4927 detection_loss_cls: 0.4927 2024/07/12 16:22:26 - mmengine - INFO - Iter(train) [ 55750/120000] base_lr: 1.1200e-04 lr: 1.2000e-05 eta: 2 days, 2:25:48 time: 2.8112 data_time: 0.1587 memory: 14084 grad_norm: 0.6025 loss: 0.4928 detection_loss_cls: 0.4928 2024/07/12 16:24:45 - mmengine - INFO - Iter(train) [ 55800/120000] base_lr: 1.1187e-04 lr: 1.1988e-05 eta: 2 days, 2:20:37 time: 2.8112 data_time: 0.1586 memory: 14084 grad_norm: 0.6029 loss: 0.4929 detection_loss_cls: 0.4929 2024/07/12 16:27:04 - mmengine - INFO - Iter(train) [ 55850/120000] base_lr: 1.1174e-04 lr: 1.1976e-05 eta: 2 days, 2:16:01 time: 2.8105 data_time: 0.1583 memory: 14084 grad_norm: 0.6033 loss: 0.4923 detection_loss_cls: 0.4923 2024/07/12 16:29:24 - mmengine - INFO - Iter(train) [ 55900/120000] base_lr: 1.1161e-04 lr: 1.1964e-05 eta: 2 days, 2:12:03 time: 2.8102 data_time: 0.1584 memory: 14084 grad_norm: 0.6030 loss: 0.4926 detection_loss_cls: 0.4926 2024/07/12 16:31:45 - mmengine - INFO - Iter(train) [ 55950/120000] base_lr: 1.1148e-04 lr: 1.1953e-05 eta: 2 days, 2:09:03 time: 2.8101 data_time: 0.1581 memory: 14084 grad_norm: 0.6032 loss: 0.4923 detection_loss_cls: 0.4923 2024/07/12 16:34:04 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240712_154442 2024/07/12 16:34:04 - mmengine - INFO - Iter(train) [ 56000/120000] base_lr: 1.1135e-04 lr: 1.1941e-05 eta: 2 days, 2:05:16 time: 2.8100 data_time: 0.1584 memory: 14084 grad_norm: 0.6033 loss: 0.4924 detection_loss_cls: 0.4924 2024/07/12 16:34:04 - mmengine - INFO - Saving checkpoint at 56000 iterations 2024/07/12 17:40:13 - mmengine - INFO - Iter(train) [ 56050/120000] base_lr: 1.1122e-04 lr: 1.1929e-05 eta: 2 days, 3:11:29 time: 2.8093 data_time: 0.1571 memory: 14086 grad_norm: 0.6034 loss: 0.4931 detection_loss_cls: 0.4931 2024/07/12 17:42:34 - mmengine - INFO - Iter(train) [ 56100/120000] base_lr: 1.1109e-04 lr: 1.1918e-05 eta: 2 days, 2:40:02 time: 2.8096 data_time: 0.1571 memory: 14086 grad_norm: 0.6035 loss: 0.4925 detection_loss_cls: 0.4925 2024/07/12 17:44:56 - mmengine - INFO - Iter(train) [ 56150/120000] base_lr: 1.1096e-04 lr: 1.1906e-05 eta: 2 days, 2:28:56 time: 2.8099 data_time: 0.1576 memory: 14086 grad_norm: 0.6035 loss: 0.4932 detection_loss_cls: 0.4932 2024/07/12 17:47:18 - mmengine - INFO - Iter(train) [ 56200/120000] base_lr: 1.1084e-04 lr: 1.1894e-05 eta: 2 days, 2:28:14 time: 2.8106 data_time: 0.1576 memory: 14086 grad_norm: 0.6036 loss: 0.4927 detection_loss_cls: 0.4927 2024/07/12 17:49:38 - mmengine - INFO - Iter(train) [ 56250/120000] base_lr: 1.1071e-04 lr: 1.1882e-05 eta: 2 days, 2:14:46 time: 2.8107 data_time: 0.1573 memory: 14086 grad_norm: 0.6036 loss: 0.4921 detection_loss_cls: 0.4921 2024/07/12 17:52:00 - mmengine - INFO - Iter(train) [ 56300/120000] base_lr: 1.1058e-04 lr: 1.1871e-05 eta: 2 days, 2:10:36 time: 2.8110 data_time: 0.1571 memory: 14086 grad_norm: 0.6037 loss: 0.4920 detection_loss_cls: 0.4920 2024/07/12 17:54:22 - mmengine - INFO - Iter(train) [ 56350/120000] base_lr: 1.1045e-04 lr: 1.1859e-05 eta: 2 days, 2:11:24 time: 2.8116 data_time: 0.1571 memory: 14086 grad_norm: 0.6036 loss: 0.4925 detection_loss_cls: 0.4925 2024/07/12 17:56:43 - mmengine - INFO - Iter(train) [ 56400/120000] base_lr: 1.1032e-04 lr: 1.1847e-05 eta: 2 days, 2:05:40 time: 2.8118 data_time: 0.1571 memory: 14086 grad_norm: 0.6035 loss: 0.4924 detection_loss_cls: 0.4924 2024/07/12 17:59:04 - mmengine - INFO - Iter(train) [ 56450/120000] base_lr: 1.1019e-04 lr: 1.1835e-05 eta: 2 days, 2:01:27 time: 2.8119 data_time: 0.1570 memory: 14086 grad_norm: 0.6034 loss: 0.4929 detection_loss_cls: 0.4929 2024/07/12 18:01:24 - mmengine - INFO - Iter(train) [ 56500/120000] base_lr: 1.1006e-04 lr: 1.1824e-05 eta: 2 days, 1:56:13 time: 2.8118 data_time: 0.1568 memory: 14086 grad_norm: 0.6035 loss: 0.4925 detection_loss_cls: 0.4925 2024/07/12 18:03:43 - mmengine - INFO - Iter(train) [ 56550/120000] base_lr: 1.0993e-04 lr: 1.1812e-05 eta: 2 days, 1:48:37 time: 2.8115 data_time: 0.1568 memory: 14086 grad_norm: 0.6037 loss: 0.4928 detection_loss_cls: 0.4928 2024/07/12 18:06:04 - mmengine - INFO - Iter(train) [ 56600/120000] base_lr: 1.0980e-04 lr: 1.1800e-05 eta: 2 days, 1:44:38 time: 2.8117 data_time: 0.1566 memory: 14086 grad_norm: 0.6039 loss: 0.4925 detection_loss_cls: 0.4925 2024/07/12 18:08:24 - mmengine - INFO - Iter(train) [ 56650/120000] base_lr: 1.0967e-04 lr: 1.1789e-05 eta: 2 days, 1:40:56 time: 2.8118 data_time: 0.1568 memory: 14086 grad_norm: 0.6048 loss: 0.4927 detection_loss_cls: 0.4927 2024/07/12 18:10:44 - mmengine - INFO - Iter(train) [ 56700/120000] base_lr: 1.0954e-04 lr: 1.1777e-05 eta: 2 days, 1:36:21 time: 2.8117 data_time: 0.1567 memory: 14086 grad_norm: 0.6050 loss: 0.4927 detection_loss_cls: 0.4927 2024/07/12 18:13:04 - mmengine - INFO - Iter(train) [ 56750/120000] base_lr: 1.0942e-04 lr: 1.1765e-05 eta: 2 days, 1:32:51 time: 2.8119 data_time: 0.1567 memory: 14086 grad_norm: 0.6050 loss: 0.4928 detection_loss_cls: 0.4928 2024/07/12 18:15:25 - mmengine - INFO - Iter(train) [ 56800/120000] base_lr: 1.0929e-04 lr: 1.1753e-05 eta: 2 days, 1:30:06 time: 2.8120 data_time: 0.1569 memory: 14086 grad_norm: 0.6051 loss: 0.4936 detection_loss_cls: 0.4936 2024/07/12 18:17:44 - mmengine - INFO - Iter(train) [ 56850/120000] base_lr: 1.0916e-04 lr: 1.1742e-05 eta: 2 days, 1:25:56 time: 2.8118 data_time: 0.1569 memory: 14086 grad_norm: 0.6052 loss: 0.4937 detection_loss_cls: 0.4937 2024/07/12 18:20:04 - mmengine - INFO - Iter(train) [ 56900/120000] base_lr: 1.0903e-04 lr: 1.1730e-05 eta: 2 days, 1:22:22 time: 2.8119 data_time: 0.1562 memory: 14086 grad_norm: 0.6053 loss: 0.4928 detection_loss_cls: 0.4928 2024/07/12 18:22:24 - mmengine - INFO - Iter(train) [ 56950/120000] base_lr: 1.0890e-04 lr: 1.1718e-05 eta: 2 days, 1:18:48 time: 2.8117 data_time: 0.1562 memory: 14086 grad_norm: 0.6050 loss: 0.4930 detection_loss_cls: 0.4930 2024/07/12 18:24:44 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240712_173524 2024/07/12 18:24:44 - mmengine - INFO - Iter(train) [ 57000/120000] base_lr: 1.0877e-04 lr: 1.1706e-05 eta: 2 days, 1:15:18 time: 2.8116 data_time: 0.1561 memory: 14086 grad_norm: 0.6049 loss: 0.4933 detection_loss_cls: 0.4933 2024/07/12 18:24:44 - mmengine - INFO - Saving checkpoint at 57000 iterations 2024/07/12 18:27:12 - mmengine - INFO - Iter(train) [ 57050/120000] base_lr: 1.0864e-04 lr: 1.1695e-05 eta: 2 days, 1:20:15 time: 2.8117 data_time: 0.1561 memory: 14086 grad_norm: 0.6049 loss: 0.4934 detection_loss_cls: 0.4934 2024/07/12 18:29:32 - mmengine - INFO - Iter(train) [ 57100/120000] base_lr: 1.0851e-04 lr: 1.1683e-05 eta: 2 days, 1:17:16 time: 2.8118 data_time: 0.1557 memory: 14086 grad_norm: 0.6047 loss: 0.4924 detection_loss_cls: 0.4924 2024/07/12 18:31:53 - mmengine - INFO - Iter(train) [ 57150/120000] base_lr: 1.0838e-04 lr: 1.1671e-05 eta: 2 days, 1:14:29 time: 2.8118 data_time: 0.1554 memory: 14086 grad_norm: 0.6049 loss: 0.4922 detection_loss_cls: 0.4922 2024/07/12 18:34:12 - 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mmengine - INFO - Saving checkpoint at 60000 iterations 2024/07/13 04:09:54 - mmengine - INFO - Iter(train) [ 60050/120000] base_lr: 1.0087e-04 lr: 1.0988e-05 eta: 2 days, 0:55:58 time: 2.8101 data_time: 0.1589 memory: 14089 grad_norm: 0.6029 loss: 0.4838 detection_loss_cls: 0.4838 2024/07/13 04:12:19 - mmengine - INFO - Iter(train) [ 60100/120000] base_lr: 1.0074e-04 lr: 1.0977e-05 eta: 2 days, 0:25:05 time: 2.8107 data_time: 0.1592 memory: 14089 grad_norm: 0.6028 loss: 0.4846 detection_loss_cls: 0.4846 2024/07/13 04:14:43 - mmengine - INFO - Iter(train) [ 60150/120000] base_lr: 1.0061e-04 lr: 1.0965e-05 eta: 2 days, 0:13:27 time: 2.8114 data_time: 0.1592 memory: 14089 grad_norm: 0.6029 loss: 0.4847 detection_loss_cls: 0.4847 2024/07/13 04:17:05 - mmengine - INFO - Iter(train) [ 60200/120000] base_lr: 1.0048e-04 lr: 1.0953e-05 eta: 1 day, 23:55:58 time: 2.8112 data_time: 0.1594 memory: 14089 grad_norm: 0.6028 loss: 0.4849 detection_loss_cls: 0.4849 2024/07/13 04:19:28 - mmengine - INFO - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_040502 2024/07/13 04:54:28 - mmengine - INFO - Iter(train) [ 61000/120000] base_lr: 9.8411e-05 lr: 1.0765e-05 eta: 1 day, 22:13:17 time: 2.8108 data_time: 0.1583 memory: 14089 grad_norm: 0.6027 loss: 0.4829 detection_loss_cls: 0.4829 2024/07/13 04:54:28 - mmengine - INFO - Saving checkpoint at 61000 iterations 2024/07/13 04:56:56 - mmengine - INFO - Iter(train) [ 61050/120000] base_lr: 9.8282e-05 lr: 1.0753e-05 eta: 1 day, 22:17:43 time: 2.8108 data_time: 0.1581 memory: 14089 grad_norm: 0.6027 loss: 0.4824 detection_loss_cls: 0.4824 2024/07/13 04:59:16 - mmengine - INFO - Iter(train) [ 61100/120000] base_lr: 9.8152e-05 lr: 1.0741e-05 eta: 1 day, 22:13:50 time: 2.8107 data_time: 0.1580 memory: 14089 grad_norm: 0.6028 loss: 0.4826 detection_loss_cls: 0.4826 2024/07/13 05:01:36 - mmengine - INFO - Iter(train) [ 61150/120000] base_lr: 9.8022e-05 lr: 1.0729e-05 eta: 1 day, 22:10:37 time: 2.8106 data_time: 0.1578 memory: 14089 grad_norm: 0.6026 loss: 0.4827 detection_loss_cls: 0.4827 2024/07/13 05:03:56 - 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mmengine - INFO - Iter(train) [ 61950/120000] base_lr: 9.5951e-05 lr: 1.0541e-05 eta: 1 day, 21:21:43 time: 2.8104 data_time: 0.1547 memory: 14089 grad_norm: 0.6046 loss: 0.4813 detection_loss_cls: 0.4813 2024/07/13 05:41:13 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_040502 2024/07/13 05:41:13 - mmengine - INFO - Iter(train) [ 62000/120000] base_lr: 9.5821e-05 lr: 1.0529e-05 eta: 1 day, 21:18:50 time: 2.8103 data_time: 0.1546 memory: 14089 grad_norm: 0.6045 loss: 0.4814 detection_loss_cls: 0.4814 2024/07/13 05:41:13 - mmengine - INFO - Saving checkpoint at 62000 iterations 2024/07/13 05:43:42 - mmengine - INFO - Iter(train) [ 62050/120000] base_lr: 9.5692e-05 lr: 1.0517e-05 eta: 1 day, 21:20:02 time: 2.8102 data_time: 0.1550 memory: 14089 grad_norm: 0.6044 loss: 0.4818 detection_loss_cls: 0.4818 2024/07/13 05:46:01 - mmengine - INFO - Iter(train) [ 62100/120000] base_lr: 9.5563e-05 lr: 1.0506e-05 eta: 1 day, 21:17:10 time: 2.8100 data_time: 0.1547 memory: 14084 grad_norm: 0.6045 loss: 0.4817 detection_loss_cls: 0.4817 2024/07/13 05:48:21 - 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mmengine - INFO - Saving checkpoint at 64000 iterations 2024/07/13 07:17:08 - mmengine - INFO - Iter(train) [ 64050/120000] base_lr: 9.0525e-05 lr: 1.0048e-05 eta: 1 day, 19:39:54 time: 2.8079 data_time: 0.1525 memory: 14089 grad_norm: 0.6085 loss: 0.4814 detection_loss_cls: 0.4814 2024/07/13 07:19:28 - mmengine - INFO - Iter(train) [ 64100/120000] base_lr: 9.0397e-05 lr: 1.0036e-05 eta: 1 day, 19:37:32 time: 2.8070 data_time: 0.1523 memory: 14089 grad_norm: 0.6087 loss: 0.4813 detection_loss_cls: 0.4813 2024/07/13 07:21:49 - mmengine - INFO - Iter(train) [ 64150/120000] base_lr: 9.0268e-05 lr: 1.0024e-05 eta: 1 day, 19:35:21 time: 2.8063 data_time: 0.1519 memory: 14089 grad_norm: 0.6084 loss: 0.4806 detection_loss_cls: 0.4806 2024/07/13 07:24:09 - mmengine - INFO - Iter(train) [ 64200/120000] base_lr: 9.0139e-05 lr: 1.0013e-05 eta: 1 day, 19:32:55 time: 2.8058 data_time: 0.1517 memory: 14089 grad_norm: 0.6084 loss: 0.4806 detection_loss_cls: 0.4806 2024/07/13 07:26:29 - mmengine - INFO - 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mmengine - INFO - Iter(train) [ 64500/120000] base_lr: 8.9366e-05 lr: 9.9424e-06 eta: 1 day, 19:18:29 time: 2.8045 data_time: 0.1516 memory: 14089 grad_norm: 0.6092 loss: 0.4813 detection_loss_cls: 0.4813 2024/07/13 07:40:31 - mmengine - INFO - Iter(train) [ 64550/120000] base_lr: 8.9238e-05 lr: 9.9307e-06 eta: 1 day, 19:16:15 time: 2.8049 data_time: 0.1517 memory: 14089 grad_norm: 0.6089 loss: 0.4815 detection_loss_cls: 0.4815 2024/07/13 07:42:52 - mmengine - INFO - Iter(train) [ 64600/120000] base_lr: 8.9109e-05 lr: 9.9190e-06 eta: 1 day, 19:13:52 time: 2.8051 data_time: 0.1516 memory: 14089 grad_norm: 0.6091 loss: 0.4814 detection_loss_cls: 0.4814 2024/07/13 07:45:12 - mmengine - INFO - Iter(train) [ 64650/120000] base_lr: 8.8980e-05 lr: 9.9073e-06 eta: 1 day, 19:11:24 time: 2.8050 data_time: 0.1515 memory: 14089 grad_norm: 0.6092 loss: 0.4811 detection_loss_cls: 0.4811 2024/07/13 07:47:31 - mmengine - INFO - Iter(train) [ 64700/120000] base_lr: 8.8852e-05 lr: 9.8956e-06 eta: 1 day, 19:08:57 time: 2.8052 data_time: 0.1518 memory: 14089 grad_norm: 0.6093 loss: 0.4814 detection_loss_cls: 0.4814 2024/07/13 07:49:52 - mmengine - INFO - Iter(train) [ 64750/120000] base_lr: 8.8723e-05 lr: 9.8839e-06 eta: 1 day, 19:06:41 time: 2.8054 data_time: 0.1516 memory: 14089 grad_norm: 0.6093 loss: 0.4806 detection_loss_cls: 0.4806 2024/07/13 07:52:12 - mmengine - INFO - Iter(train) [ 64800/120000] base_lr: 8.8595e-05 lr: 9.8722e-06 eta: 1 day, 19:04:16 time: 2.8053 data_time: 0.1515 memory: 14089 grad_norm: 0.6094 loss: 0.4804 detection_loss_cls: 0.4804 2024/07/13 07:54:33 - mmengine - INFO - Iter(train) [ 64850/120000] base_lr: 8.8466e-05 lr: 9.8605e-06 eta: 1 day, 19:01:59 time: 2.8056 data_time: 0.1518 memory: 14089 grad_norm: 0.6095 loss: 0.4806 detection_loss_cls: 0.4806 2024/07/13 07:56:53 - mmengine - INFO - Iter(train) [ 64900/120000] base_lr: 8.8337e-05 lr: 9.8489e-06 eta: 1 day, 18:59:26 time: 2.8055 data_time: 0.1520 memory: 14089 grad_norm: 0.6097 loss: 0.4809 detection_loss_cls: 0.4809 2024/07/13 07:59:12 - mmengine - INFO - Iter(train) [ 64950/120000] base_lr: 8.8209e-05 lr: 9.8372e-06 eta: 1 day, 18:56:59 time: 2.8054 data_time: 0.1519 memory: 14089 grad_norm: 0.6098 loss: 0.4806 detection_loss_cls: 0.4806 2024/07/13 08:01:32 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_040502 2024/07/13 08:01:32 - mmengine - INFO - Iter(train) [ 65000/120000] base_lr: 8.8080e-05 lr: 9.8255e-06 eta: 1 day, 18:54:31 time: 2.8056 data_time: 0.1518 memory: 14089 grad_norm: 0.6097 loss: 0.4805 detection_loss_cls: 0.4805 2024/07/13 08:01:32 - mmengine - INFO - Saving checkpoint at 65000 iterations 2024/07/13 08:02:01 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:29 time: 0.4128 data_time: 0.0048 memory: 5704 2024/07/13 08:02:22 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:05 time: 0.4127 data_time: 0.0048 memory: 5704 2024/07/13 08:02:43 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:45 time: 0.4129 data_time: 0.0048 memory: 5704 2024/07/13 08:03:03 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:23 time: 0.4128 data_time: 0.0048 memory: 5704 2024/07/13 08:03:24 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:01 time: 0.4128 data_time: 0.0048 memory: 5704 2024/07/13 08:03:44 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:40 time: 0.4127 data_time: 0.0048 memory: 5704 2024/07/13 08:04:05 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:19 time: 0.4127 data_time: 0.0048 memory: 5704 2024/07/13 08:04:26 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:59 time: 0.4128 data_time: 0.0048 memory: 5704 2024/07/13 08:04:46 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:38 time: 0.4127 data_time: 0.0047 memory: 5704 2024/07/13 08:05:06 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:17 time: 0.4127 data_time: 0.0047 memory: 5704 2024/07/13 08:05:27 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4126 data_time: 0.0047 memory: 5704 2024/07/13 08:05:47 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:36 time: 0.4126 data_time: 0.0047 memory: 5704 2024/07/13 08:06:08 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4126 data_time: 0.0047 memory: 5704 2024/07/13 08:06:28 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:55 time: 0.4126 data_time: 0.0047 memory: 5704 2024/07/13 08:06:49 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4126 data_time: 0.0047 memory: 5704 2024/07/13 08:07:10 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4126 data_time: 0.0047 memory: 5704 2024/07/13 08:07:25 - mmengine - INFO - Evaluating bbox... 2024/07/13 08:07:51 - mmengine - INFO - bbox_mAP_copypaste: 0.452 0.618 0.491 0.266 0.492 0.610 2024/07/13 08:07:52 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4520 coco/bbox_mAP_50: 0.6180 coco/bbox_mAP_75: 0.4910 coco/bbox_mAP_s: 0.2660 coco/bbox_mAP_m: 0.4920 coco/bbox_mAP_l: 0.6100 data_time: 0.0047 time: 0.4112 2024/07/13 11:52:18 - mmengine - INFO - Iter(train) [ 65050/120000] base_lr: 8.7952e-05 lr: 9.8138e-06 eta: 1 day, 19:46:14 time: 2.8044 data_time: 0.1501 memory: 14083 grad_norm: 0.6096 loss: 0.4805 detection_loss_cls: 0.4805 2024/07/13 11:54:40 - mmengine - INFO - Iter(train) [ 65100/120000] base_lr: 8.7824e-05 lr: 9.8021e-06 eta: 1 day, 19:27:15 time: 2.8049 data_time: 0.1505 memory: 14083 grad_norm: 0.6098 loss: 0.4801 detection_loss_cls: 0.4801 2024/07/13 11:57:02 - mmengine - INFO - Iter(train) [ 65150/120000] base_lr: 8.7695e-05 lr: 9.7905e-06 eta: 1 day, 19:20:09 time: 2.8053 data_time: 0.1510 memory: 14083 grad_norm: 0.6099 loss: 0.4801 detection_loss_cls: 0.4801 2024/07/13 11:59:22 - mmengine - INFO - Iter(train) [ 65200/120000] base_lr: 8.7567e-05 lr: 9.7788e-06 eta: 1 day, 19:11:06 time: 2.8055 data_time: 0.1515 memory: 14083 grad_norm: 0.6095 loss: 0.4799 detection_loss_cls: 0.4799 2024/07/13 12:01:46 - mmengine - INFO - Iter(train) [ 65250/120000] base_lr: 8.7438e-05 lr: 9.7671e-06 eta: 1 day, 19:14:47 time: 2.8064 data_time: 0.1524 memory: 14083 grad_norm: 0.6096 loss: 0.4815 detection_loss_cls: 0.4815 2024/07/13 12:04:05 - mmengine - INFO - Iter(train) [ 65300/120000] base_lr: 8.7310e-05 lr: 9.7555e-06 eta: 1 day, 19:04:07 time: 2.8062 data_time: 0.1528 memory: 14083 grad_norm: 0.6093 loss: 0.4818 detection_loss_cls: 0.4818 2024/07/13 12:06:26 - mmengine - INFO - Iter(train) [ 65350/120000] base_lr: 8.7182e-05 lr: 9.7438e-06 eta: 1 day, 18:59:10 time: 2.8062 data_time: 0.1534 memory: 14083 grad_norm: 0.6091 loss: 0.4826 detection_loss_cls: 0.4826 2024/07/13 12:08:47 - mmengine - INFO - Iter(train) [ 65400/120000] base_lr: 8.7053e-05 lr: 9.7321e-06 eta: 1 day, 18:53:53 time: 2.8064 data_time: 0.1537 memory: 14083 grad_norm: 0.6091 loss: 0.4825 detection_loss_cls: 0.4825 2024/07/13 12:11:07 - mmengine - INFO - Iter(train) [ 65450/120000] base_lr: 8.6925e-05 lr: 9.7205e-06 eta: 1 day, 18:49:43 time: 2.8063 data_time: 0.1537 memory: 14083 grad_norm: 0.6091 loss: 0.4820 detection_loss_cls: 0.4820 2024/07/13 12:13:27 - mmengine - INFO - Iter(train) [ 65500/120000] base_lr: 8.6797e-05 lr: 9.7088e-06 eta: 1 day, 18:44:45 time: 2.8064 data_time: 0.1539 memory: 14083 grad_norm: 0.6094 loss: 0.4816 detection_loss_cls: 0.4816 2024/07/13 12:15:49 - mmengine - INFO - Iter(train) [ 65550/120000] base_lr: 8.6669e-05 lr: 9.6971e-06 eta: 1 day, 18:44:00 time: 2.8073 data_time: 0.1543 memory: 14083 grad_norm: 0.6088 loss: 0.4816 detection_loss_cls: 0.4816 2024/07/13 12:18:08 - mmengine - INFO - Iter(train) [ 65600/120000] base_lr: 8.6540e-05 lr: 9.6855e-06 eta: 1 day, 18:38:28 time: 2.8071 data_time: 0.1545 memory: 14083 grad_norm: 0.6089 loss: 0.4813 detection_loss_cls: 0.4813 2024/07/13 12:20:29 - mmengine - INFO - Iter(train) [ 65650/120000] base_lr: 8.6412e-05 lr: 9.6738e-06 eta: 1 day, 18:34:51 time: 2.8072 data_time: 0.1551 memory: 14083 grad_norm: 0.6088 loss: 0.4820 detection_loss_cls: 0.4820 2024/07/13 12:22:48 - mmengine - INFO - Iter(train) [ 65700/120000] base_lr: 8.6284e-05 lr: 9.6622e-06 eta: 1 day, 18:30:16 time: 2.8070 data_time: 0.1556 memory: 14083 grad_norm: 0.6091 loss: 0.4824 detection_loss_cls: 0.4824 2024/07/13 12:25:07 - mmengine - INFO - Iter(train) [ 65750/120000] base_lr: 8.6156e-05 lr: 9.6505e-06 eta: 1 day, 18:25:14 time: 2.8069 data_time: 0.1559 memory: 14083 grad_norm: 0.6093 loss: 0.4824 detection_loss_cls: 0.4824 2024/07/13 12:27:26 - mmengine - INFO - Iter(train) [ 65800/120000] base_lr: 8.6028e-05 lr: 9.6389e-06 eta: 1 day, 18:21:25 time: 2.8070 data_time: 0.1559 memory: 14083 grad_norm: 0.6093 loss: 0.4817 detection_loss_cls: 0.4817 2024/07/13 12:29:46 - mmengine - INFO - Iter(train) [ 65850/120000] base_lr: 8.5900e-05 lr: 9.6272e-06 eta: 1 day, 18:18:19 time: 2.8070 data_time: 0.1561 memory: 14082 grad_norm: 0.6096 loss: 0.4812 detection_loss_cls: 0.4812 2024/07/13 12:32:05 - mmengine - INFO - Iter(train) [ 65900/120000] base_lr: 8.5772e-05 lr: 9.6156e-06 eta: 1 day, 18:14:04 time: 2.8067 data_time: 0.1564 memory: 14083 grad_norm: 0.6095 loss: 0.4811 detection_loss_cls: 0.4811 2024/07/13 12:34:25 - mmengine - INFO - Iter(train) [ 65950/120000] base_lr: 8.5644e-05 lr: 9.6040e-06 eta: 1 day, 18:11:13 time: 2.8067 data_time: 0.1565 memory: 14083 grad_norm: 0.6095 loss: 0.4815 detection_loss_cls: 0.4815 2024/07/13 12:36:45 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_114729 2024/07/13 12:36:45 - mmengine - INFO - Iter(train) [ 66000/120000] base_lr: 8.5516e-05 lr: 9.5923e-06 eta: 1 day, 18:08:35 time: 2.8068 data_time: 0.1569 memory: 14083 grad_norm: 0.6095 loss: 0.4819 detection_loss_cls: 0.4819 2024/07/13 12:36:45 - mmengine - INFO - Saving checkpoint at 66000 iterations 2024/07/13 12:39:13 - mmengine - INFO - Iter(train) [ 66050/120000] base_lr: 8.5388e-05 lr: 9.5807e-06 eta: 1 day, 18:13:01 time: 2.8069 data_time: 0.1571 memory: 14083 grad_norm: 0.6096 loss: 0.4819 detection_loss_cls: 0.4819 2024/07/13 12:41:34 - mmengine - INFO - Iter(train) [ 66100/120000] base_lr: 8.5260e-05 lr: 9.5691e-06 eta: 1 day, 18:10:16 time: 2.8070 data_time: 0.1574 memory: 14083 grad_norm: 0.6094 loss: 0.4817 detection_loss_cls: 0.4817 2024/07/13 12:43:55 - mmengine - INFO - Iter(train) [ 66150/120000] base_lr: 8.5132e-05 lr: 9.5574e-06 eta: 1 day, 18:07:55 time: 2.8074 data_time: 0.1576 memory: 14083 grad_norm: 0.6095 loss: 0.4821 detection_loss_cls: 0.4821 2024/07/13 12:46:14 - mmengine - INFO - Iter(train) [ 66200/120000] base_lr: 8.5004e-05 lr: 9.5458e-06 eta: 1 day, 18:04:51 time: 2.8072 data_time: 0.1580 memory: 14083 grad_norm: 0.6096 loss: 0.4824 detection_loss_cls: 0.4824 2024/07/13 12:48:35 - mmengine - INFO - Iter(train) [ 66250/120000] base_lr: 8.4876e-05 lr: 9.5342e-06 eta: 1 day, 18:02:08 time: 2.8073 data_time: 0.1582 memory: 14088 grad_norm: 0.6096 loss: 0.4823 detection_loss_cls: 0.4823 2024/07/13 12:50:56 - mmengine - INFO - Iter(train) [ 66300/120000] base_lr: 8.4748e-05 lr: 9.5226e-06 eta: 1 day, 17:59:56 time: 2.8076 data_time: 0.1588 memory: 14083 grad_norm: 0.6099 loss: 0.4830 detection_loss_cls: 0.4830 2024/07/13 12:53:16 - mmengine - INFO - Iter(train) [ 66350/120000] base_lr: 8.4620e-05 lr: 9.5109e-06 eta: 1 day, 17:57:13 time: 2.8078 data_time: 0.1591 memory: 14083 grad_norm: 0.6097 loss: 0.4832 detection_loss_cls: 0.4832 2024/07/13 12:55:36 - mmengine - INFO - Iter(train) [ 66400/120000] base_lr: 8.4492e-05 lr: 9.4993e-06 eta: 1 day, 17:54:12 time: 2.8076 data_time: 0.1594 memory: 14083 grad_norm: 0.6097 loss: 0.4841 detection_loss_cls: 0.4841 2024/07/13 12:57:57 - mmengine - INFO - Iter(train) [ 66450/120000] base_lr: 8.4365e-05 lr: 9.4877e-06 eta: 1 day, 17:52:22 time: 2.8080 data_time: 0.1593 memory: 14083 grad_norm: 0.6098 loss: 0.4836 detection_loss_cls: 0.4836 2024/07/13 13:00:17 - mmengine - INFO - Iter(train) [ 66500/120000] base_lr: 8.4237e-05 lr: 9.4761e-06 eta: 1 day, 17:49:25 time: 2.8080 data_time: 0.1597 memory: 14083 grad_norm: 0.6099 loss: 0.4841 detection_loss_cls: 0.4841 2024/07/13 13:02:38 - mmengine - INFO - Iter(train) [ 66550/120000] base_lr: 8.4109e-05 lr: 9.4645e-06 eta: 1 day, 17:47:00 time: 2.8080 data_time: 0.1599 memory: 14083 grad_norm: 0.6100 loss: 0.4839 detection_loss_cls: 0.4839 2024/07/13 13:04:59 - 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mmengine - INFO - Saving checkpoint at 67000 iterations 2024/07/13 13:26:05 - mmengine - INFO - Iter(train) [ 67050/120000] base_lr: 8.2834e-05 lr: 9.3485e-06 eta: 1 day, 17:23:23 time: 2.8086 data_time: 0.1619 memory: 14083 grad_norm: 0.6103 loss: 0.4832 detection_loss_cls: 0.4832 2024/07/13 13:28:27 - mmengine - INFO - Iter(train) [ 67100/120000] base_lr: 8.2706e-05 lr: 9.3370e-06 eta: 1 day, 17:21:42 time: 2.8091 data_time: 0.1622 memory: 14083 grad_norm: 0.6103 loss: 0.4831 detection_loss_cls: 0.4831 2024/07/13 13:30:48 - mmengine - INFO - Iter(train) [ 67150/120000] base_lr: 8.2579e-05 lr: 9.3254e-06 eta: 1 day, 17:19:23 time: 2.8095 data_time: 0.1625 memory: 14083 grad_norm: 0.6102 loss: 0.4835 detection_loss_cls: 0.4835 2024/07/13 13:33:08 - mmengine - INFO - Iter(train) [ 67200/120000] base_lr: 8.2452e-05 lr: 9.3138e-06 eta: 1 day, 17:16:41 time: 2.8091 data_time: 0.1625 memory: 14083 grad_norm: 0.6103 loss: 0.4829 detection_loss_cls: 0.4829 2024/07/13 13:35:27 - mmengine - INFO - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_114729 2024/07/13 14:10:34 - mmengine - INFO - Iter(train) [ 68000/120000] base_lr: 8.0419e-05 lr: 9.1290e-06 eta: 1 day, 16:37:45 time: 2.8127 data_time: 0.1665 memory: 14083 grad_norm: 0.6102 loss: 0.4815 detection_loss_cls: 0.4815 2024/07/13 14:10:34 - mmengine - INFO - Saving checkpoint at 68000 iterations 2024/07/13 14:13:03 - mmengine - INFO - Iter(train) [ 68050/120000] base_lr: 8.0293e-05 lr: 9.1175e-06 eta: 1 day, 16:37:30 time: 2.8127 data_time: 0.1669 memory: 14083 grad_norm: 0.6099 loss: 0.4818 detection_loss_cls: 0.4818 2024/07/13 14:15:23 - mmengine - INFO - Iter(train) [ 68100/120000] base_lr: 8.0166e-05 lr: 9.1060e-06 eta: 1 day, 16:34:59 time: 2.8126 data_time: 0.1669 memory: 14083 grad_norm: 0.6097 loss: 0.4816 detection_loss_cls: 0.4816 2024/07/13 14:17:43 - mmengine - INFO - Iter(train) [ 68150/120000] base_lr: 8.0039e-05 lr: 9.0945e-06 eta: 1 day, 16:32:30 time: 2.8124 data_time: 0.1674 memory: 14083 grad_norm: 0.6099 loss: 0.4821 detection_loss_cls: 0.4821 2024/07/13 14:20:03 - 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mmengine - INFO - Iter(train) [ 69900/120000] base_lr: 7.5630e-05 lr: 8.6936e-06 eta: 1 day, 15:09:07 time: 2.8140 data_time: 0.1716 memory: 14083 grad_norm: 0.6104 loss: 0.4749 detection_loss_cls: 0.4749 2024/07/13 15:42:02 - mmengine - INFO - Iter(train) [ 69950/120000] base_lr: 7.5505e-05 lr: 8.6822e-06 eta: 1 day, 15:06:40 time: 2.8140 data_time: 0.1715 memory: 14083 grad_norm: 0.6110 loss: 0.4742 detection_loss_cls: 0.4742 2024/07/13 15:44:23 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_114729 2024/07/13 15:44:23 - mmengine - INFO - Iter(train) [ 70000/120000] base_lr: 7.5379e-05 lr: 8.6709e-06 eta: 1 day, 15:04:23 time: 2.8142 data_time: 0.1716 memory: 14083 grad_norm: 0.6114 loss: 0.4739 detection_loss_cls: 0.4739 2024/07/13 15:44:23 - mmengine - INFO - Saving checkpoint at 70000 iterations 2024/07/13 15:44:52 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:30 time: 0.4128 data_time: 0.0048 memory: 5706 2024/07/13 15:45:13 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:05 time: 0.4128 data_time: 0.0048 memory: 5706 2024/07/13 15:45:33 - 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mmengine - INFO - Iter(val) [600/834] eta: 0:01:36 time: 0.4127 data_time: 0.0048 memory: 5706 2024/07/13 15:48:59 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4127 data_time: 0.0048 memory: 5706 2024/07/13 15:49:20 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:55 time: 0.4128 data_time: 0.0048 memory: 5706 2024/07/13 15:49:40 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4127 data_time: 0.0048 memory: 5706 2024/07/13 15:50:01 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:14 time: 0.4127 data_time: 0.0048 memory: 5706 2024/07/13 15:50:21 - mmengine - INFO - Evaluating bbox... 2024/07/13 15:50:47 - mmengine - INFO - bbox_mAP_copypaste: 0.454 0.619 0.494 0.266 0.493 0.613 2024/07/13 15:50:48 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4540 coco/bbox_mAP_50: 0.6190 coco/bbox_mAP_75: 0.4940 coco/bbox_mAP_s: 0.2660 coco/bbox_mAP_m: 0.4930 coco/bbox_mAP_l: 0.6130 data_time: 0.0049 time: 0.4118 2024/07/13 15:53:08 - mmengine - INFO - Iter(train) [ 70050/120000] base_lr: 7.5254e-05 lr: 8.6595e-06 eta: 1 day, 15:07:22 time: 2.8203 data_time: 0.1775 memory: 14137 grad_norm: 0.6115 loss: 0.4735 detection_loss_cls: 0.4735 2024/07/13 15:55:26 - mmengine - INFO - Iter(train) [ 70100/120000] base_lr: 7.5129e-05 lr: 8.6481e-06 eta: 1 day, 15:04:37 time: 2.8199 data_time: 0.1778 memory: 14137 grad_norm: 0.6115 loss: 0.4739 detection_loss_cls: 0.4739 2024/07/13 15:57:45 - mmengine - INFO - Iter(train) [ 70150/120000] base_lr: 7.5004e-05 lr: 8.6367e-06 eta: 1 day, 15:01:59 time: 2.8195 data_time: 0.1780 memory: 14137 grad_norm: 0.6118 loss: 0.4740 detection_loss_cls: 0.4740 2024/07/13 16:00:06 - mmengine - INFO - Iter(train) [ 70200/120000] base_lr: 7.4879e-05 lr: 8.6254e-06 eta: 1 day, 14:59:32 time: 2.8196 data_time: 0.1778 memory: 14137 grad_norm: 0.6120 loss: 0.4735 detection_loss_cls: 0.4735 2024/07/13 16:02:25 - mmengine - INFO - Iter(train) [ 70250/120000] base_lr: 7.4754e-05 lr: 8.6140e-06 eta: 1 day, 14:56:56 time: 2.8194 data_time: 0.1778 memory: 14137 grad_norm: 0.6119 loss: 0.4736 detection_loss_cls: 0.4736 2024/07/13 16:04:44 - mmengine - INFO - Iter(train) [ 70300/120000] base_lr: 7.4629e-05 lr: 8.6027e-06 eta: 1 day, 14:54:15 time: 2.8188 data_time: 0.1776 memory: 14137 grad_norm: 0.6117 loss: 0.4736 detection_loss_cls: 0.4736 2024/07/13 16:07:04 - mmengine - INFO - Iter(train) [ 70350/120000] base_lr: 7.4504e-05 lr: 8.5913e-06 eta: 1 day, 14:51:51 time: 2.8189 data_time: 0.1774 memory: 14137 grad_norm: 0.6119 loss: 0.4731 detection_loss_cls: 0.4731 2024/07/13 16:09:24 - mmengine - INFO - Iter(train) [ 70400/120000] base_lr: 7.4379e-05 lr: 8.5799e-06 eta: 1 day, 14:49:17 time: 2.8188 data_time: 0.1772 memory: 14137 grad_norm: 0.6124 loss: 0.4723 detection_loss_cls: 0.4723 2024/07/13 16:11:43 - mmengine - INFO - Iter(train) [ 70450/120000] base_lr: 7.4255e-05 lr: 8.5686e-06 eta: 1 day, 14:46:41 time: 2.8183 data_time: 0.1771 memory: 14137 grad_norm: 0.6124 loss: 0.4718 detection_loss_cls: 0.4718 2024/07/13 16:14:03 - mmengine - INFO - Iter(train) [ 70500/120000] base_lr: 7.4130e-05 lr: 8.5573e-06 eta: 1 day, 14:44:09 time: 2.8182 data_time: 0.1774 memory: 14137 grad_norm: 0.6124 loss: 0.4722 detection_loss_cls: 0.4722 2024/07/13 16:16:23 - mmengine - INFO - Iter(train) [ 70550/120000] base_lr: 7.4005e-05 lr: 8.5459e-06 eta: 1 day, 14:41:47 time: 2.8183 data_time: 0.1776 memory: 14137 grad_norm: 0.6130 loss: 0.4723 detection_loss_cls: 0.4723 2024/07/13 16:18:43 - mmengine - INFO - Iter(train) [ 70600/120000] base_lr: 7.3881e-05 lr: 8.5346e-06 eta: 1 day, 14:39:16 time: 2.8179 data_time: 0.1771 memory: 14137 grad_norm: 0.6130 loss: 0.4714 detection_loss_cls: 0.4714 2024/07/13 16:21:04 - mmengine - INFO - Iter(train) [ 70650/120000] base_lr: 7.3756e-05 lr: 8.5233e-06 eta: 1 day, 14:36:52 time: 2.8181 data_time: 0.1770 memory: 14137 grad_norm: 0.6134 loss: 0.4713 detection_loss_cls: 0.4713 2024/07/13 16:23:23 - mmengine - INFO - Iter(train) [ 70700/120000] base_lr: 7.3631e-05 lr: 8.5119e-06 eta: 1 day, 14:34:19 time: 2.8182 data_time: 0.1769 memory: 14137 grad_norm: 0.6132 loss: 0.4713 detection_loss_cls: 0.4713 2024/07/13 16:25:44 - mmengine - INFO - Iter(train) [ 70750/120000] base_lr: 7.3507e-05 lr: 8.5006e-06 eta: 1 day, 14:31:59 time: 2.8185 data_time: 0.1771 memory: 14137 grad_norm: 0.6140 loss: 0.4716 detection_loss_cls: 0.4716 2024/07/13 16:28:03 - mmengine - INFO - Iter(train) [ 70800/120000] base_lr: 7.3382e-05 lr: 8.4893e-06 eta: 1 day, 14:29:25 time: 2.8184 data_time: 0.1770 memory: 14137 grad_norm: 0.6138 loss: 0.4717 detection_loss_cls: 0.4717 2024/07/13 16:30:23 - mmengine - INFO - Iter(train) [ 70850/120000] base_lr: 7.3258e-05 lr: 8.4780e-06 eta: 1 day, 14:26:53 time: 2.8184 data_time: 0.1772 memory: 14137 grad_norm: 0.6140 loss: 0.4720 detection_loss_cls: 0.4720 2024/07/13 16:32:43 - mmengine - INFO - Iter(train) [ 70900/120000] base_lr: 7.3134e-05 lr: 8.4667e-06 eta: 1 day, 14:24:29 time: 2.8183 data_time: 0.1772 memory: 14137 grad_norm: 0.6140 loss: 0.4718 detection_loss_cls: 0.4718 2024/07/13 16:35:03 - mmengine - INFO - Iter(train) [ 70950/120000] base_lr: 7.3009e-05 lr: 8.4554e-06 eta: 1 day, 14:22:02 time: 2.8184 data_time: 0.1772 memory: 14137 grad_norm: 0.6140 loss: 0.4721 detection_loss_cls: 0.4721 2024/07/13 16:37:23 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_114729 2024/07/13 16:37:23 - mmengine - INFO - Iter(train) [ 71000/120000] base_lr: 7.2885e-05 lr: 8.4441e-06 eta: 1 day, 14:19:33 time: 2.8184 data_time: 0.1774 memory: 14137 grad_norm: 0.6142 loss: 0.4725 detection_loss_cls: 0.4725 2024/07/13 16:37:23 - mmengine - INFO - Saving checkpoint at 71000 iterations 2024/07/13 16:39:50 - mmengine - INFO - Iter(train) [ 71050/120000] base_lr: 7.2761e-05 lr: 8.4328e-06 eta: 1 day, 14:18:01 time: 2.8182 data_time: 0.1776 memory: 14137 grad_norm: 0.6146 loss: 0.4730 detection_loss_cls: 0.4730 2024/07/13 16:42:09 - mmengine - INFO - Iter(train) [ 71100/120000] base_lr: 7.2637e-05 lr: 8.4215e-06 eta: 1 day, 14:15:27 time: 2.8174 data_time: 0.1776 memory: 14137 grad_norm: 0.6142 loss: 0.4733 detection_loss_cls: 0.4733 2024/07/13 16:44:30 - mmengine - INFO - Iter(train) [ 71150/120000] base_lr: 7.2512e-05 lr: 8.4102e-06 eta: 1 day, 14:13:04 time: 2.8174 data_time: 0.1774 memory: 14137 grad_norm: 0.6143 loss: 0.4724 detection_loss_cls: 0.4724 2024/07/13 16:46:49 - mmengine - INFO - Iter(train) [ 71200/120000] base_lr: 7.2388e-05 lr: 8.3989e-06 eta: 1 day, 14:10:33 time: 2.8173 data_time: 0.1773 memory: 14137 grad_norm: 0.6144 loss: 0.4719 detection_loss_cls: 0.4719 2024/07/13 16:49:09 - mmengine - INFO - Iter(train) [ 71250/120000] base_lr: 7.2264e-05 lr: 8.3877e-06 eta: 1 day, 14:08:03 time: 2.8172 data_time: 0.1771 memory: 14137 grad_norm: 0.6142 loss: 0.4719 detection_loss_cls: 0.4719 2024/07/13 16:51:28 - mmengine - INFO - Iter(train) [ 71300/120000] base_lr: 7.2140e-05 lr: 8.3764e-06 eta: 1 day, 14:05:27 time: 2.8168 data_time: 0.1772 memory: 14137 grad_norm: 0.6146 loss: 0.4719 detection_loss_cls: 0.4719 2024/07/13 16:53:47 - mmengine - INFO - Iter(train) [ 71350/120000] base_lr: 7.2016e-05 lr: 8.3651e-06 eta: 1 day, 14:02:54 time: 2.8163 data_time: 0.1770 memory: 14137 grad_norm: 0.6147 loss: 0.4715 detection_loss_cls: 0.4715 2024/07/13 16:56:07 - mmengine - INFO - Iter(train) [ 71400/120000] base_lr: 7.1892e-05 lr: 8.3539e-06 eta: 1 day, 14:00:22 time: 2.8161 data_time: 0.1769 memory: 14137 grad_norm: 0.6148 loss: 0.4710 detection_loss_cls: 0.4710 2024/07/13 16:58:26 - mmengine - INFO - Iter(train) [ 71450/120000] base_lr: 7.1769e-05 lr: 8.3426e-06 eta: 1 day, 13:57:55 time: 2.8161 data_time: 0.1768 memory: 14137 grad_norm: 0.6143 loss: 0.4709 detection_loss_cls: 0.4709 2024/07/13 17:00:46 - mmengine - INFO - Iter(train) [ 71500/120000] base_lr: 7.1645e-05 lr: 8.3314e-06 eta: 1 day, 13:55:28 time: 2.8158 data_time: 0.1767 memory: 14137 grad_norm: 0.6143 loss: 0.4709 detection_loss_cls: 0.4709 2024/07/13 17:03:06 - mmengine - INFO - Iter(train) [ 71550/120000] base_lr: 7.1521e-05 lr: 8.3201e-06 eta: 1 day, 13:52:58 time: 2.8153 data_time: 0.1766 memory: 14137 grad_norm: 0.6142 loss: 0.4709 detection_loss_cls: 0.4709 2024/07/13 17:05:25 - mmengine - INFO - Iter(train) [ 71600/120000] base_lr: 7.1397e-05 lr: 8.3089e-06 eta: 1 day, 13:50:22 time: 2.8150 data_time: 0.1767 memory: 14137 grad_norm: 0.6139 loss: 0.4712 detection_loss_cls: 0.4712 2024/07/13 17:07:43 - mmengine - INFO - Iter(train) [ 71650/120000] base_lr: 7.1274e-05 lr: 8.2976e-06 eta: 1 day, 13:47:48 time: 2.8143 data_time: 0.1764 memory: 14137 grad_norm: 0.6138 loss: 0.4710 detection_loss_cls: 0.4710 2024/07/13 17:10:03 - mmengine - INFO - Iter(train) [ 71700/120000] base_lr: 7.1150e-05 lr: 8.2864e-06 eta: 1 day, 13:45:17 time: 2.8140 data_time: 0.1768 memory: 14137 grad_norm: 0.6138 loss: 0.4714 detection_loss_cls: 0.4714 2024/07/13 17:12:23 - mmengine - INFO - Iter(train) [ 71750/120000] base_lr: 7.1027e-05 lr: 8.2752e-06 eta: 1 day, 13:42:53 time: 2.8143 data_time: 0.1769 memory: 14137 grad_norm: 0.6138 loss: 0.4713 detection_loss_cls: 0.4713 2024/07/13 17:14:42 - mmengine - INFO - Iter(train) [ 71800/120000] base_lr: 7.0903e-05 lr: 8.2639e-06 eta: 1 day, 13:40:20 time: 2.8138 data_time: 0.1770 memory: 14137 grad_norm: 0.6140 loss: 0.4712 detection_loss_cls: 0.4712 2024/07/13 17:17:03 - mmengine - INFO - Iter(train) [ 71850/120000] base_lr: 7.0780e-05 lr: 8.2527e-06 eta: 1 day, 13:37:58 time: 2.8140 data_time: 0.1768 memory: 14137 grad_norm: 0.6136 loss: 0.4706 detection_loss_cls: 0.4706 2024/07/13 17:19:22 - mmengine - INFO - Iter(train) [ 71900/120000] base_lr: 7.0656e-05 lr: 8.2415e-06 eta: 1 day, 13:35:28 time: 2.8137 data_time: 0.1766 memory: 14137 grad_norm: 0.6139 loss: 0.4701 detection_loss_cls: 0.4701 2024/07/13 17:21:43 - mmengine - INFO - Iter(train) [ 71950/120000] base_lr: 7.0533e-05 lr: 8.2303e-06 eta: 1 day, 13:33:07 time: 2.8140 data_time: 0.1768 memory: 14137 grad_norm: 0.6136 loss: 0.4701 detection_loss_cls: 0.4701 2024/07/13 17:24:04 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_114729 2024/07/13 17:24:04 - mmengine - INFO - Iter(train) [ 72000/120000] base_lr: 7.0410e-05 lr: 8.2191e-06 eta: 1 day, 13:30:53 time: 2.8142 data_time: 0.1769 memory: 14137 grad_norm: 0.6137 loss: 0.4702 detection_loss_cls: 0.4702 2024/07/13 17:24:04 - mmengine - INFO - Saving checkpoint at 72000 iterations 2024/07/13 17:26:32 - mmengine - INFO - Iter(train) [ 72050/120000] base_lr: 7.0287e-05 lr: 8.2079e-06 eta: 1 day, 13:29:23 time: 2.8143 data_time: 0.1768 memory: 14137 grad_norm: 0.6140 loss: 0.4696 detection_loss_cls: 0.4696 2024/07/13 17:28:52 - mmengine - INFO - Iter(train) [ 72100/120000] base_lr: 7.0163e-05 lr: 8.1967e-06 eta: 1 day, 13:26:52 time: 2.8140 data_time: 0.1767 memory: 14137 grad_norm: 0.6141 loss: 0.4694 detection_loss_cls: 0.4694 2024/07/13 17:31:10 - mmengine - INFO - Iter(train) [ 72150/120000] base_lr: 7.0040e-05 lr: 8.1855e-06 eta: 1 day, 13:24:19 time: 2.8137 data_time: 0.1766 memory: 14137 grad_norm: 0.6143 loss: 0.4695 detection_loss_cls: 0.4695 2024/07/13 17:33:31 - mmengine - INFO - Iter(train) [ 72200/120000] base_lr: 6.9917e-05 lr: 8.1743e-06 eta: 1 day, 13:22:00 time: 2.8138 data_time: 0.1767 memory: 14137 grad_norm: 0.6144 loss: 0.4699 detection_loss_cls: 0.4699 2024/07/13 17:35:53 - mmengine - INFO - Iter(train) [ 72250/120000] base_lr: 6.9794e-05 lr: 8.1631e-06 eta: 1 day, 13:19:42 time: 2.8145 data_time: 0.1768 memory: 14137 grad_norm: 0.6150 loss: 0.4695 detection_loss_cls: 0.4695 2024/07/13 17:38:14 - mmengine - INFO - Iter(train) [ 72300/120000] base_lr: 6.9671e-05 lr: 8.1519e-06 eta: 1 day, 13:17:24 time: 2.8145 data_time: 0.1771 memory: 14137 grad_norm: 0.6151 loss: 0.4700 detection_loss_cls: 0.4700 2024/07/13 17:40:33 - mmengine - INFO - Iter(train) [ 72350/120000] base_lr: 6.9548e-05 lr: 8.1408e-06 eta: 1 day, 13:14:56 time: 2.8143 data_time: 0.1770 memory: 14137 grad_norm: 0.6151 loss: 0.4698 detection_loss_cls: 0.4698 2024/07/13 17:42:53 - mmengine - INFO - Iter(train) [ 72400/120000] base_lr: 6.9425e-05 lr: 8.1296e-06 eta: 1 day, 13:12:29 time: 2.8142 data_time: 0.1772 memory: 14137 grad_norm: 0.6153 loss: 0.4703 detection_loss_cls: 0.4703 2024/07/13 17:45:13 - mmengine - INFO - Iter(train) [ 72450/120000] base_lr: 6.9303e-05 lr: 8.1184e-06 eta: 1 day, 13:10:00 time: 2.8142 data_time: 0.1774 memory: 14137 grad_norm: 0.6156 loss: 0.4707 detection_loss_cls: 0.4707 2024/07/13 17:47:33 - mmengine - INFO - Iter(train) [ 72500/120000] base_lr: 6.9180e-05 lr: 8.1073e-06 eta: 1 day, 13:07:40 time: 2.8141 data_time: 0.1775 memory: 14137 grad_norm: 0.6154 loss: 0.4710 detection_loss_cls: 0.4710 2024/07/13 17:49:53 - mmengine - INFO - Iter(train) [ 72550/120000] base_lr: 6.9057e-05 lr: 8.0961e-06 eta: 1 day, 13:05:15 time: 2.8142 data_time: 0.1776 memory: 14137 grad_norm: 0.6153 loss: 0.4713 detection_loss_cls: 0.4713 2024/07/13 17:52:13 - mmengine - INFO - Iter(train) [ 72600/120000] base_lr: 6.8935e-05 lr: 8.0850e-06 eta: 1 day, 13:02:47 time: 2.8138 data_time: 0.1773 memory: 14137 grad_norm: 0.6154 loss: 0.4709 detection_loss_cls: 0.4709 2024/07/13 17:54:32 - mmengine - INFO - Iter(train) [ 72650/120000] base_lr: 6.8812e-05 lr: 8.0738e-06 eta: 1 day, 13:00:19 time: 2.8134 data_time: 0.1773 memory: 14137 grad_norm: 0.6154 loss: 0.4709 detection_loss_cls: 0.4709 2024/07/13 17:56:52 - mmengine - INFO - Iter(train) [ 72700/120000] base_lr: 6.8690e-05 lr: 8.0627e-06 eta: 1 day, 12:57:53 time: 2.8131 data_time: 0.1776 memory: 14137 grad_norm: 0.6157 loss: 0.4716 detection_loss_cls: 0.4716 2024/07/13 17:59:12 - mmengine - INFO - Iter(train) [ 72750/120000] base_lr: 6.8567e-05 lr: 8.0516e-06 eta: 1 day, 12:55:29 time: 2.8130 data_time: 0.1776 memory: 14137 grad_norm: 0.6157 loss: 0.4715 detection_loss_cls: 0.4715 2024/07/13 18:01:31 - mmengine - INFO - Iter(train) [ 72800/120000] base_lr: 6.8445e-05 lr: 8.0404e-06 eta: 1 day, 12:52:57 time: 2.8129 data_time: 0.1778 memory: 14137 grad_norm: 0.6157 loss: 0.4721 detection_loss_cls: 0.4721 2024/07/13 18:03:53 - mmengine - INFO - Iter(train) [ 72850/120000] base_lr: 6.8322e-05 lr: 8.0293e-06 eta: 1 day, 12:50:45 time: 2.8135 data_time: 0.1779 memory: 14137 grad_norm: 0.6155 loss: 0.4723 detection_loss_cls: 0.4723 2024/07/13 18:06:12 - mmengine - INFO - Iter(train) [ 72900/120000] base_lr: 6.8200e-05 lr: 8.0182e-06 eta: 1 day, 12:48:15 time: 2.8133 data_time: 0.1777 memory: 14137 grad_norm: 0.6156 loss: 0.4721 detection_loss_cls: 0.4721 2024/07/13 18:08:32 - mmengine - INFO - Iter(train) [ 72950/120000] base_lr: 6.8078e-05 lr: 8.0071e-06 eta: 1 day, 12:45:47 time: 2.8130 data_time: 0.1778 memory: 14137 grad_norm: 0.6156 loss: 0.4726 detection_loss_cls: 0.4726 2024/07/13 18:10:52 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_114729 2024/07/13 18:10:52 - mmengine - INFO - Iter(train) [ 73000/120000] base_lr: 6.7956e-05 lr: 7.9960e-06 eta: 1 day, 12:43:24 time: 2.8129 data_time: 0.1776 memory: 14137 grad_norm: 0.6158 loss: 0.4721 detection_loss_cls: 0.4721 2024/07/13 18:10:52 - mmengine - INFO - Saving checkpoint at 73000 iterations 2024/07/13 18:13:19 - mmengine - INFO - Iter(train) [ 73050/120000] base_lr: 6.7833e-05 lr: 7.9849e-06 eta: 1 day, 12:41:37 time: 2.8123 data_time: 0.1773 memory: 14137 grad_norm: 0.6166 loss: 0.4718 detection_loss_cls: 0.4718 2024/07/13 18:15:39 - mmengine - INFO - Iter(train) [ 73100/120000] base_lr: 6.7711e-05 lr: 7.9738e-06 eta: 1 day, 12:39:13 time: 2.8121 data_time: 0.1775 memory: 14137 grad_norm: 0.6168 loss: 0.4726 detection_loss_cls: 0.4726 2024/07/13 18:17:58 - 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mmengine - INFO - Iter(train) [ 73900/120000] base_lr: 6.5766e-05 lr: 7.7969e-06 eta: 1 day, 12:00:45 time: 2.8110 data_time: 0.1771 memory: 14137 grad_norm: 0.6192 loss: 0.4726 detection_loss_cls: 0.4726 2024/07/13 18:55:19 - mmengine - INFO - Iter(train) [ 73950/120000] base_lr: 6.5645e-05 lr: 7.7859e-06 eta: 1 day, 11:58:22 time: 2.8110 data_time: 0.1774 memory: 14137 grad_norm: 0.6189 loss: 0.4735 detection_loss_cls: 0.4735 2024/07/13 18:57:38 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_114729 2024/07/13 18:57:38 - mmengine - INFO - Iter(train) [ 74000/120000] base_lr: 6.5524e-05 lr: 7.7749e-06 eta: 1 day, 11:55:54 time: 2.8105 data_time: 0.1772 memory: 14137 grad_norm: 0.6186 loss: 0.4733 detection_loss_cls: 0.4733 2024/07/13 18:57:38 - mmengine - INFO - Saving checkpoint at 74000 iterations 2024/07/13 19:00:05 - mmengine - INFO - Iter(train) [ 74050/120000] base_lr: 6.5403e-05 lr: 7.7639e-06 eta: 1 day, 11:54:07 time: 2.8041 data_time: 0.1710 memory: 14137 grad_norm: 0.6185 loss: 0.4737 detection_loss_cls: 0.4737 2024/07/13 19:02:25 - mmengine - INFO - Iter(train) [ 74100/120000] base_lr: 6.5282e-05 lr: 7.7529e-06 eta: 1 day, 11:51:44 time: 2.8045 data_time: 0.1706 memory: 14137 grad_norm: 0.6186 loss: 0.4732 detection_loss_cls: 0.4732 2024/07/13 19:04:44 - mmengine - INFO - Iter(train) [ 74150/120000] base_lr: 6.5161e-05 lr: 7.7419e-06 eta: 1 day, 11:49:15 time: 2.8044 data_time: 0.1702 memory: 14137 grad_norm: 0.6183 loss: 0.4727 detection_loss_cls: 0.4727 2024/07/13 19:07:06 - mmengine - INFO - Iter(train) [ 74200/120000] base_lr: 6.5041e-05 lr: 7.7310e-06 eta: 1 day, 11:46:58 time: 2.8047 data_time: 0.1704 memory: 14137 grad_norm: 0.6182 loss: 0.4731 detection_loss_cls: 0.4731 2024/07/13 19:09:24 - mmengine - INFO - Iter(train) [ 74250/120000] base_lr: 6.4920e-05 lr: 7.7200e-06 eta: 1 day, 11:44:27 time: 2.8045 data_time: 0.1705 memory: 14137 grad_norm: 0.6184 loss: 0.4729 detection_loss_cls: 0.4729 2024/07/13 19:11:43 - mmengine - INFO - Iter(train) [ 74300/120000] base_lr: 6.4799e-05 lr: 7.7090e-06 eta: 1 day, 11:41:58 time: 2.8045 data_time: 0.1703 memory: 14137 grad_norm: 0.6184 loss: 0.4723 detection_loss_cls: 0.4723 2024/07/13 19:14:03 - mmengine - INFO - Iter(train) [ 74350/120000] base_lr: 6.4679e-05 lr: 7.6981e-06 eta: 1 day, 11:39:31 time: 2.8042 data_time: 0.1708 memory: 14137 grad_norm: 0.6185 loss: 0.4727 detection_loss_cls: 0.4727 2024/07/13 19:16:23 - mmengine - INFO - Iter(train) [ 74400/120000] base_lr: 6.4558e-05 lr: 7.6871e-06 eta: 1 day, 11:37:10 time: 2.8045 data_time: 0.1708 memory: 14137 grad_norm: 0.6185 loss: 0.4725 detection_loss_cls: 0.4725 2024/07/13 19:18:42 - mmengine - INFO - Iter(train) [ 74450/120000] base_lr: 6.4438e-05 lr: 7.6761e-06 eta: 1 day, 11:34:41 time: 2.8044 data_time: 0.1710 memory: 14137 grad_norm: 0.6184 loss: 0.4728 detection_loss_cls: 0.4728 2024/07/13 19:21:02 - mmengine - INFO - Iter(train) [ 74500/120000] base_lr: 6.4317e-05 lr: 7.6652e-06 eta: 1 day, 11:32:18 time: 2.8045 data_time: 0.1709 memory: 14137 grad_norm: 0.6185 loss: 0.4726 detection_loss_cls: 0.4726 2024/07/13 19:23:21 - mmengine - INFO - Iter(train) [ 74550/120000] base_lr: 6.4197e-05 lr: 7.6543e-06 eta: 1 day, 11:29:49 time: 2.8040 data_time: 0.1707 memory: 14137 grad_norm: 0.6179 loss: 0.4728 detection_loss_cls: 0.4728 2024/07/13 19:25:41 - mmengine - INFO - Iter(train) [ 74600/120000] base_lr: 6.4077e-05 lr: 7.6433e-06 eta: 1 day, 11:27:24 time: 2.8040 data_time: 0.1709 memory: 14137 grad_norm: 0.6195 loss: 0.4730 detection_loss_cls: 0.4730 2024/07/13 19:28:00 - mmengine - INFO - Iter(train) [ 74650/120000] base_lr: 6.3956e-05 lr: 7.6324e-06 eta: 1 day, 11:24:59 time: 2.8038 data_time: 0.1710 memory: 14137 grad_norm: 0.6196 loss: 0.4728 detection_loss_cls: 0.4728 2024/07/13 19:30:20 - mmengine - INFO - Iter(train) [ 74700/120000] base_lr: 6.3836e-05 lr: 7.6215e-06 eta: 1 day, 11:22:33 time: 2.8038 data_time: 0.1711 memory: 14137 grad_norm: 0.6200 loss: 0.4726 detection_loss_cls: 0.4726 2024/07/13 19:32:41 - mmengine - INFO - Iter(train) [ 74750/120000] base_lr: 6.3716e-05 lr: 7.6106e-06 eta: 1 day, 11:20:13 time: 2.8038 data_time: 0.1714 memory: 14137 grad_norm: 0.6194 loss: 0.4730 detection_loss_cls: 0.4730 2024/07/13 19:35:01 - mmengine - INFO - Iter(train) [ 74800/120000] base_lr: 6.3596e-05 lr: 7.5997e-06 eta: 1 day, 11:17:52 time: 2.8041 data_time: 0.1712 memory: 14137 grad_norm: 0.6198 loss: 0.4727 detection_loss_cls: 0.4727 2024/07/13 19:37:20 - mmengine - INFO - Iter(train) [ 74850/120000] base_lr: 6.3476e-05 lr: 7.5887e-06 eta: 1 day, 11:15:26 time: 2.8040 data_time: 0.1708 memory: 14137 grad_norm: 0.6200 loss: 0.4717 detection_loss_cls: 0.4717 2024/07/13 19:39:39 - mmengine - INFO - Iter(train) [ 74900/120000] base_lr: 6.3356e-05 lr: 7.5778e-06 eta: 1 day, 11:12:58 time: 2.8036 data_time: 0.1706 memory: 14137 grad_norm: 0.6198 loss: 0.4713 detection_loss_cls: 0.4713 2024/07/13 19:42:00 - mmengine - INFO - Iter(train) [ 74950/120000] base_lr: 6.3236e-05 lr: 7.5670e-06 eta: 1 day, 11:10:38 time: 2.8038 data_time: 0.1703 memory: 14137 grad_norm: 0.6201 loss: 0.4704 detection_loss_cls: 0.4704 2024/07/13 19:44:19 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240713_114729 2024/07/13 19:44:19 - mmengine - INFO - Iter(train) [ 75000/120000] base_lr: 6.3117e-05 lr: 7.5561e-06 eta: 1 day, 11:08:12 time: 2.8037 data_time: 0.1704 memory: 14137 grad_norm: 0.6201 loss: 0.4705 detection_loss_cls: 0.4705 2024/07/13 19:44:19 - mmengine - INFO - Saving checkpoint at 75000 iterations 2024/07/13 19:44:48 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:22 time: 0.4127 data_time: 0.0048 memory: 5706 2024/07/13 19:45:08 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:00 time: 0.4126 data_time: 0.0048 memory: 5706 2024/07/13 19:45:29 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:39 time: 0.4126 data_time: 0.0048 memory: 5706 2024/07/13 19:45:49 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:18 time: 0.4125 data_time: 0.0048 memory: 5706 2024/07/13 19:46:09 - mmengine - INFO - Iter(val) [250/834] eta: 0:03:58 time: 0.4124 data_time: 0.0048 memory: 5706 2024/07/13 19:46:29 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:37 time: 0.4123 data_time: 0.0048 memory: 5706 2024/07/13 19:46:50 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:17 time: 0.4123 data_time: 0.0048 memory: 5706 2024/07/13 19:47:10 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:56 time: 0.4123 data_time: 0.0048 memory: 5706 2024/07/13 19:47:31 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:36 time: 0.4122 data_time: 0.0048 memory: 5706 2024/07/13 19:47:51 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:16 time: 0.4121 data_time: 0.0048 memory: 5706 2024/07/13 19:48:11 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:55 time: 0.4120 data_time: 0.0048 memory: 5706 2024/07/13 19:48:32 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:35 time: 0.4120 data_time: 0.0048 memory: 5706 2024/07/13 19:48:52 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:14 time: 0.4120 data_time: 0.0048 memory: 5706 2024/07/13 19:49:13 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4120 data_time: 0.0048 memory: 5706 2024/07/13 19:49:33 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4119 data_time: 0.0048 memory: 5706 2024/07/13 19:49:53 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4118 data_time: 0.0048 memory: 5706 2024/07/13 19:50:09 - mmengine - INFO - Evaluating bbox... 2024/07/13 19:50:34 - mmengine - INFO - bbox_mAP_copypaste: 0.455 0.621 0.494 0.266 0.495 0.620 2024/07/13 19:50:35 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4550 coco/bbox_mAP_50: 0.6210 coco/bbox_mAP_75: 0.4940 coco/bbox_mAP_s: 0.2660 coco/bbox_mAP_m: 0.4950 coco/bbox_mAP_l: 0.6200 data_time: 0.0046 time: 0.4077 2024/07/14 00:23:39 - mmengine - INFO - Iter(train) [ 75050/120000] base_lr: 6.2997e-05 lr: 7.5452e-06 eta: 1 day, 11:45:06 time: 2.8028 data_time: 0.1683 memory: 14083 grad_norm: 0.6199 loss: 0.4699 detection_loss_cls: 0.4699 2024/07/14 00:26:04 - mmengine - INFO - Iter(train) [ 75100/120000] base_lr: 6.2877e-05 lr: 7.5343e-06 eta: 1 day, 11:52:47 time: 2.8041 data_time: 0.1682 memory: 14083 grad_norm: 0.6199 loss: 0.4696 detection_loss_cls: 0.4696 2024/07/14 00:28:27 - mmengine - INFO - Iter(train) [ 75150/120000] base_lr: 6.2758e-05 lr: 7.5234e-06 eta: 1 day, 11:45:56 time: 2.8047 data_time: 0.1680 memory: 14083 grad_norm: 0.6197 loss: 0.4695 detection_loss_cls: 0.4695 2024/07/14 00:30:50 - mmengine - INFO - Iter(train) [ 75200/120000] base_lr: 6.2638e-05 lr: 7.5126e-06 eta: 1 day, 11:43:21 time: 2.8057 data_time: 0.1681 memory: 14083 grad_norm: 0.6198 loss: 0.4698 detection_loss_cls: 0.4698 2024/07/14 00:33:14 - mmengine - INFO - Iter(train) [ 75250/120000] base_lr: 6.2519e-05 lr: 7.5017e-06 eta: 1 day, 11:41:35 time: 2.8067 data_time: 0.1680 memory: 14083 grad_norm: 0.6199 loss: 0.4696 detection_loss_cls: 0.4696 2024/07/14 00:35:35 - mmengine - INFO - Iter(train) [ 75300/120000] base_lr: 6.2400e-05 lr: 7.4909e-06 eta: 1 day, 11:31:35 time: 2.8071 data_time: 0.1677 memory: 14083 grad_norm: 0.6201 loss: 0.4693 detection_loss_cls: 0.4693 2024/07/14 00:37:56 - mmengine - INFO - Iter(train) [ 75350/120000] base_lr: 6.2280e-05 lr: 7.4800e-06 eta: 1 day, 11:24:44 time: 2.8076 data_time: 0.1675 memory: 14083 grad_norm: 0.6203 loss: 0.4693 detection_loss_cls: 0.4693 2024/07/14 00:40:17 - mmengine - INFO - Iter(train) [ 75400/120000] base_lr: 6.2161e-05 lr: 7.4692e-06 eta: 1 day, 11:20:04 time: 2.8081 data_time: 0.1676 memory: 14083 grad_norm: 0.6203 loss: 0.4699 detection_loss_cls: 0.4699 2024/07/14 00:42:38 - 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mmengine - INFO - Iter(train) [ 76900/120000] base_lr: 5.8616e-05 lr: 7.1469e-06 eta: 1 day, 9:47:56 time: 2.8125 data_time: 0.1609 memory: 14083 grad_norm: 0.6232 loss: 0.4669 detection_loss_cls: 0.4669 2024/07/14 01:53:02 - mmengine - INFO - Iter(train) [ 76950/120000] base_lr: 5.8499e-05 lr: 7.1362e-06 eta: 1 day, 9:45:25 time: 2.8128 data_time: 0.1606 memory: 14083 grad_norm: 0.6233 loss: 0.4667 detection_loss_cls: 0.4667 2024/07/14 01:55:22 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 01:55:22 - mmengine - INFO - Iter(train) [ 77000/120000] base_lr: 5.8382e-05 lr: 7.1256e-06 eta: 1 day, 9:42:39 time: 2.8128 data_time: 0.1605 memory: 14083 grad_norm: 0.6232 loss: 0.4667 detection_loss_cls: 0.4667 2024/07/14 01:55:22 - mmengine - INFO - Saving checkpoint at 77000 iterations 2024/07/14 01:57:49 - mmengine - INFO - Iter(train) [ 77050/120000] base_lr: 5.8265e-05 lr: 7.1150e-06 eta: 1 day, 9:42:21 time: 2.8129 data_time: 0.1605 memory: 14083 grad_norm: 0.6227 loss: 0.4665 detection_loss_cls: 0.4665 2024/07/14 02:00:09 - 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mmengine - INFO - Saving checkpoint at 78000 iterations 2024/07/14 02:44:45 - mmengine - INFO - Iter(train) [ 78050/120000] base_lr: 5.5942e-05 lr: 6.9038e-06 eta: 1 day, 8:53:02 time: 2.8152 data_time: 0.1558 memory: 14083 grad_norm: 0.6243 loss: 0.4636 detection_loss_cls: 0.4636 2024/07/14 02:47:05 - mmengine - INFO - Iter(train) [ 78100/120000] base_lr: 5.5826e-05 lr: 6.8933e-06 eta: 1 day, 8:50:30 time: 2.8152 data_time: 0.1560 memory: 14083 grad_norm: 0.6244 loss: 0.4642 detection_loss_cls: 0.4642 2024/07/14 02:49:26 - mmengine - INFO - Iter(train) [ 78150/120000] base_lr: 5.5711e-05 lr: 6.8828e-06 eta: 1 day, 8:48:06 time: 2.8157 data_time: 0.1559 memory: 14083 grad_norm: 0.6243 loss: 0.4641 detection_loss_cls: 0.4641 2024/07/14 02:51:46 - mmengine - INFO - Iter(train) [ 78200/120000] base_lr: 5.5596e-05 lr: 6.8724e-06 eta: 1 day, 8:45:38 time: 2.8155 data_time: 0.1554 memory: 14083 grad_norm: 0.6245 loss: 0.4635 detection_loss_cls: 0.4635 2024/07/14 02:54:07 - mmengine - INFO - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 03:29:11 - mmengine - INFO - Iter(train) [ 79000/120000] base_lr: 5.3764e-05 lr: 6.7058e-06 eta: 1 day, 8:05:49 time: 2.8183 data_time: 0.1513 memory: 14082 grad_norm: 0.6249 loss: 0.4614 detection_loss_cls: 0.4614 2024/07/14 03:29:11 - mmengine - INFO - Saving checkpoint at 79000 iterations 2024/07/14 03:31:39 - mmengine - INFO - Iter(train) [ 79050/120000] base_lr: 5.3650e-05 lr: 6.6954e-06 eta: 1 day, 8:04:43 time: 2.8196 data_time: 0.1533 memory: 14083 grad_norm: 0.6250 loss: 0.4618 detection_loss_cls: 0.4618 2024/07/14 03:34:00 - mmengine - INFO - Iter(train) [ 79100/120000] base_lr: 5.3536e-05 lr: 6.6851e-06 eta: 1 day, 8:02:22 time: 2.8187 data_time: 0.1531 memory: 14083 grad_norm: 0.6252 loss: 0.4619 detection_loss_cls: 0.4619 2024/07/14 03:36:19 - mmengine - INFO - Iter(train) [ 79150/120000] base_lr: 5.3422e-05 lr: 6.6748e-06 eta: 1 day, 7:59:42 time: 2.8177 data_time: 0.1531 memory: 14083 grad_norm: 0.6255 loss: 0.4624 detection_loss_cls: 0.4624 2024/07/14 03:38:41 - mmengine - INFO - Iter(train) [ 79200/120000] base_lr: 5.3309e-05 lr: 6.6644e-06 eta: 1 day, 7:57:23 time: 2.8172 data_time: 0.1529 memory: 14083 grad_norm: 0.6255 loss: 0.4625 detection_loss_cls: 0.4625 2024/07/14 03:41:02 - mmengine - INFO - Iter(train) [ 79250/120000] base_lr: 5.3195e-05 lr: 6.6541e-06 eta: 1 day, 7:55:01 time: 2.8164 data_time: 0.1527 memory: 14082 grad_norm: 0.6258 loss: 0.4626 detection_loss_cls: 0.4626 2024/07/14 03:43:23 - mmengine - INFO - Iter(train) [ 79300/120000] base_lr: 5.3082e-05 lr: 6.6438e-06 eta: 1 day, 7:52:41 time: 2.8166 data_time: 0.1527 memory: 14083 grad_norm: 0.6256 loss: 0.4629 detection_loss_cls: 0.4629 2024/07/14 03:45:43 - mmengine - INFO - Iter(train) [ 79350/120000] base_lr: 5.2968e-05 lr: 6.6335e-06 eta: 1 day, 7:50:14 time: 2.8164 data_time: 0.1529 memory: 14083 grad_norm: 0.6260 loss: 0.4634 detection_loss_cls: 0.4634 2024/07/14 03:48:05 - mmengine - INFO - Iter(train) [ 79400/120000] base_lr: 5.2855e-05 lr: 6.6232e-06 eta: 1 day, 7:47:59 time: 2.8165 data_time: 0.1524 memory: 14083 grad_norm: 0.6261 loss: 0.4628 detection_loss_cls: 0.4628 2024/07/14 03:50:25 - mmengine - INFO - Iter(train) [ 79450/120000] base_lr: 5.2742e-05 lr: 6.6129e-06 eta: 1 day, 7:45:31 time: 2.8164 data_time: 0.1521 memory: 14083 grad_norm: 0.6261 loss: 0.4623 detection_loss_cls: 0.4623 2024/07/14 03:52:44 - mmengine - INFO - Iter(train) [ 79500/120000] base_lr: 5.2629e-05 lr: 6.6026e-06 eta: 1 day, 7:42:54 time: 2.8160 data_time: 0.1520 memory: 14083 grad_norm: 0.6263 loss: 0.4626 detection_loss_cls: 0.4626 2024/07/14 03:55:04 - mmengine - INFO - Iter(train) [ 79550/120000] base_lr: 5.2516e-05 lr: 6.5923e-06 eta: 1 day, 7:40:24 time: 2.8157 data_time: 0.1518 memory: 14082 grad_norm: 0.6265 loss: 0.4625 detection_loss_cls: 0.4625 2024/07/14 03:57:25 - mmengine - INFO - Iter(train) [ 79600/120000] base_lr: 5.2403e-05 lr: 6.5821e-06 eta: 1 day, 7:38:00 time: 2.8159 data_time: 0.1517 memory: 14083 grad_norm: 0.6268 loss: 0.4627 detection_loss_cls: 0.4627 2024/07/14 03:59:46 - mmengine - INFO - Iter(train) [ 79650/120000] base_lr: 5.2290e-05 lr: 6.5718e-06 eta: 1 day, 7:35:39 time: 2.8157 data_time: 0.1517 memory: 14083 grad_norm: 0.6269 loss: 0.4627 detection_loss_cls: 0.4627 2024/07/14 04:02:06 - mmengine - INFO - Iter(train) [ 79700/120000] base_lr: 5.2177e-05 lr: 6.5616e-06 eta: 1 day, 7:33:13 time: 2.8155 data_time: 0.1515 memory: 14083 grad_norm: 0.6271 loss: 0.4630 detection_loss_cls: 0.4630 2024/07/14 04:04:27 - mmengine - INFO - Iter(train) [ 79750/120000] base_lr: 5.2064e-05 lr: 6.5513e-06 eta: 1 day, 7:30:53 time: 2.8156 data_time: 0.1511 memory: 14083 grad_norm: 0.6270 loss: 0.4621 detection_loss_cls: 0.4621 2024/07/14 04:06:48 - mmengine - INFO - Iter(train) [ 79800/120000] base_lr: 5.1952e-05 lr: 6.5411e-06 eta: 1 day, 7:28:30 time: 2.8156 data_time: 0.1510 memory: 14083 grad_norm: 0.6275 loss: 0.4624 detection_loss_cls: 0.4624 2024/07/14 04:09:07 - mmengine - INFO - Iter(train) [ 79850/120000] base_lr: 5.1839e-05 lr: 6.5308e-06 eta: 1 day, 7:25:58 time: 2.8157 data_time: 0.1509 memory: 14083 grad_norm: 0.6283 loss: 0.4625 detection_loss_cls: 0.4625 2024/07/14 04:11:29 - mmengine - INFO - Iter(train) [ 79900/120000] base_lr: 5.1727e-05 lr: 6.5206e-06 eta: 1 day, 7:23:40 time: 2.8161 data_time: 0.1512 memory: 14082 grad_norm: 0.6278 loss: 0.4634 detection_loss_cls: 0.4634 2024/07/14 04:13:49 - mmengine - INFO - Iter(train) [ 79950/120000] base_lr: 5.1615e-05 lr: 6.5104e-06 eta: 1 day, 7:21:16 time: 2.8157 data_time: 0.1512 memory: 14083 grad_norm: 0.6277 loss: 0.4633 detection_loss_cls: 0.4633 2024/07/14 04:16:09 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 04:16:09 - mmengine - INFO - Iter(train) [ 80000/120000] base_lr: 5.1502e-05 lr: 6.5002e-06 eta: 1 day, 7:18:49 time: 2.8155 data_time: 0.1513 memory: 14083 grad_norm: 0.6277 loss: 0.4633 detection_loss_cls: 0.4633 2024/07/14 04:16:09 - mmengine - INFO - Saving checkpoint at 80000 iterations 2024/07/14 04:16:38 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:27 time: 0.4127 data_time: 0.0048 memory: 5705 2024/07/14 04:16:59 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:02 time: 0.4127 data_time: 0.0048 memory: 5705 2024/07/14 04:17:19 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:40 time: 0.4126 data_time: 0.0048 memory: 5705 2024/07/14 04:17:39 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:19 time: 0.4126 data_time: 0.0048 memory: 5705 2024/07/14 04:17:59 - mmengine - INFO - Iter(val) [250/834] eta: 0:03:58 time: 0.4125 data_time: 0.0048 memory: 5705 2024/07/14 04:18:20 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:37 time: 0.4124 data_time: 0.0048 memory: 5705 2024/07/14 04:18:40 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:17 time: 0.4123 data_time: 0.0048 memory: 5705 2024/07/14 04:19:00 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:56 time: 0.4123 data_time: 0.0048 memory: 5705 2024/07/14 04:19:20 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:35 time: 0.4121 data_time: 0.0048 memory: 5705 2024/07/14 04:19:40 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:15 time: 0.4120 data_time: 0.0048 memory: 5705 2024/07/14 04:20:01 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:55 time: 0.4119 data_time: 0.0048 memory: 5705 2024/07/14 04:20:21 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:35 time: 0.4119 data_time: 0.0048 memory: 5705 2024/07/14 04:20:41 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:14 time: 0.4118 data_time: 0.0048 memory: 5705 2024/07/14 04:21:02 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4118 data_time: 0.0048 memory: 5705 2024/07/14 04:21:22 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4117 data_time: 0.0048 memory: 5705 2024/07/14 04:21:43 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4117 data_time: 0.0048 memory: 5705 2024/07/14 04:21:59 - mmengine - INFO - Evaluating bbox... 2024/07/14 04:22:25 - mmengine - INFO - bbox_mAP_copypaste: 0.456 0.622 0.495 0.264 0.496 0.623 2024/07/14 04:22:25 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4560 coco/bbox_mAP_50: 0.6220 coco/bbox_mAP_75: 0.4950 coco/bbox_mAP_s: 0.2640 coco/bbox_mAP_m: 0.4960 coco/bbox_mAP_l: 0.6230 data_time: 0.0051 time: 0.4071 2024/07/14 04:24:46 - mmengine - INFO - Iter(train) [ 80050/120000] base_lr: 5.1390e-05 lr: 6.4900e-06 eta: 1 day, 7:20:08 time: 2.8209 data_time: 0.1567 memory: 14134 grad_norm: 0.6277 loss: 0.4636 detection_loss_cls: 0.4636 2024/07/14 04:27:06 - mmengine - INFO - Iter(train) [ 80100/120000] base_lr: 5.1278e-05 lr: 6.4798e-06 eta: 1 day, 7:17:40 time: 2.8208 data_time: 0.1570 memory: 14134 grad_norm: 0.6279 loss: 0.4637 detection_loss_cls: 0.4637 2024/07/14 04:29:25 - mmengine - INFO - Iter(train) [ 80150/120000] base_lr: 5.1166e-05 lr: 6.4696e-06 eta: 1 day, 7:15:06 time: 2.8202 data_time: 0.1571 memory: 14134 grad_norm: 0.6281 loss: 0.4638 detection_loss_cls: 0.4638 2024/07/14 04:31:45 - mmengine - INFO - Iter(train) [ 80200/120000] base_lr: 5.1054e-05 lr: 6.4595e-06 eta: 1 day, 7:12:32 time: 2.8198 data_time: 0.1573 memory: 14134 grad_norm: 0.6284 loss: 0.4636 detection_loss_cls: 0.4636 2024/07/14 04:34:04 - mmengine - INFO - Iter(train) [ 80250/120000] base_lr: 5.0942e-05 lr: 6.4493e-06 eta: 1 day, 7:09:58 time: 2.8197 data_time: 0.1576 memory: 14134 grad_norm: 0.6285 loss: 0.4642 detection_loss_cls: 0.4642 2024/07/14 04:36:24 - mmengine - INFO - Iter(train) [ 80300/120000] base_lr: 5.0830e-05 lr: 6.4391e-06 eta: 1 day, 7:07:23 time: 2.8197 data_time: 0.1578 memory: 14134 grad_norm: 0.6286 loss: 0.4645 detection_loss_cls: 0.4645 2024/07/14 04:38:44 - mmengine - INFO - Iter(train) [ 80350/120000] base_lr: 5.0719e-05 lr: 6.4290e-06 eta: 1 day, 7:04:56 time: 2.8197 data_time: 0.1579 memory: 14134 grad_norm: 0.6285 loss: 0.4642 detection_loss_cls: 0.4642 2024/07/14 04:41:06 - mmengine - INFO - Iter(train) [ 80400/120000] base_lr: 5.0607e-05 lr: 6.4188e-06 eta: 1 day, 7:02:38 time: 2.8201 data_time: 0.1580 memory: 14134 grad_norm: 0.6287 loss: 0.4641 detection_loss_cls: 0.4641 2024/07/14 04:43:26 - mmengine - INFO - Iter(train) [ 80450/120000] base_lr: 5.0496e-05 lr: 6.4087e-06 eta: 1 day, 7:00:09 time: 2.8200 data_time: 0.1583 memory: 14134 grad_norm: 0.6289 loss: 0.4642 detection_loss_cls: 0.4642 2024/07/14 04:45:45 - mmengine - INFO - Iter(train) [ 80500/120000] base_lr: 5.0384e-05 lr: 6.3986e-06 eta: 1 day, 6:57:33 time: 2.8196 data_time: 0.1584 memory: 14134 grad_norm: 0.6291 loss: 0.4643 detection_loss_cls: 0.4643 2024/07/14 04:48:04 - mmengine - INFO - Iter(train) [ 80550/120000] base_lr: 5.0273e-05 lr: 6.3884e-06 eta: 1 day, 6:55:00 time: 2.8193 data_time: 0.1583 memory: 14134 grad_norm: 0.6292 loss: 0.4636 detection_loss_cls: 0.4636 2024/07/14 04:50:23 - mmengine - INFO - Iter(train) [ 80600/120000] base_lr: 5.0162e-05 lr: 6.3783e-06 eta: 1 day, 6:52:23 time: 2.8190 data_time: 0.1584 memory: 14134 grad_norm: 0.6291 loss: 0.4636 detection_loss_cls: 0.4636 2024/07/14 04:52:42 - mmengine - INFO - Iter(train) [ 80650/120000] base_lr: 5.0050e-05 lr: 6.3682e-06 eta: 1 day, 6:49:48 time: 2.8188 data_time: 0.1587 memory: 14134 grad_norm: 0.6291 loss: 0.4636 detection_loss_cls: 0.4636 2024/07/14 04:55:01 - mmengine - INFO - Iter(train) [ 80700/120000] base_lr: 4.9939e-05 lr: 6.3581e-06 eta: 1 day, 6:47:16 time: 2.8186 data_time: 0.1588 memory: 14134 grad_norm: 0.6293 loss: 0.4638 detection_loss_cls: 0.4638 2024/07/14 04:57:23 - mmengine - INFO - Iter(train) [ 80750/120000] base_lr: 4.9828e-05 lr: 6.3480e-06 eta: 1 day, 6:44:56 time: 2.8189 data_time: 0.1589 memory: 14134 grad_norm: 0.6293 loss: 0.4634 detection_loss_cls: 0.4634 2024/07/14 04:59:42 - mmengine - INFO - Iter(train) [ 80800/120000] base_lr: 4.9718e-05 lr: 6.3380e-06 eta: 1 day, 6:42:24 time: 2.8185 data_time: 0.1593 memory: 14134 grad_norm: 0.6290 loss: 0.4637 detection_loss_cls: 0.4637 2024/07/14 05:02:02 - mmengine - INFO - Iter(train) [ 80850/120000] base_lr: 4.9607e-05 lr: 6.3279e-06 eta: 1 day, 6:39:57 time: 2.8182 data_time: 0.1595 memory: 14134 grad_norm: 0.6290 loss: 0.4637 detection_loss_cls: 0.4637 2024/07/14 05:04:22 - mmengine - INFO - Iter(train) [ 80900/120000] base_lr: 4.9496e-05 lr: 6.3178e-06 eta: 1 day, 6:37:27 time: 2.8180 data_time: 0.1595 memory: 14134 grad_norm: 0.6292 loss: 0.4628 detection_loss_cls: 0.4628 2024/07/14 05:06:41 - mmengine - INFO - Iter(train) [ 80950/120000] base_lr: 4.9385e-05 lr: 6.3078e-06 eta: 1 day, 6:34:55 time: 2.8176 data_time: 0.1596 memory: 14134 grad_norm: 0.6290 loss: 0.4624 detection_loss_cls: 0.4624 2024/07/14 05:09:01 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 05:09:01 - mmengine - INFO - Iter(train) [ 81000/120000] base_lr: 4.9275e-05 lr: 6.2977e-06 eta: 1 day, 6:32:29 time: 2.8177 data_time: 0.1599 memory: 14134 grad_norm: 0.6291 loss: 0.4630 detection_loss_cls: 0.4630 2024/07/14 05:09:01 - mmengine - INFO - Saving checkpoint at 81000 iterations 2024/07/14 05:11:29 - mmengine - INFO - Iter(train) [ 81050/120000] base_lr: 4.9164e-05 lr: 6.2877e-06 eta: 1 day, 6:30:53 time: 2.8179 data_time: 0.1601 memory: 14134 grad_norm: 0.6292 loss: 0.4630 detection_loss_cls: 0.4630 2024/07/14 05:13:49 - mmengine - INFO - Iter(train) [ 81100/120000] base_lr: 4.9054e-05 lr: 6.2776e-06 eta: 1 day, 6:28:23 time: 2.8177 data_time: 0.1602 memory: 14134 grad_norm: 0.6291 loss: 0.4631 detection_loss_cls: 0.4631 2024/07/14 05:16:08 - mmengine - INFO - Iter(train) [ 81150/120000] base_lr: 4.8944e-05 lr: 6.2676e-06 eta: 1 day, 6:25:52 time: 2.8179 data_time: 0.1602 memory: 14134 grad_norm: 0.6291 loss: 0.4629 detection_loss_cls: 0.4629 2024/07/14 05:18:28 - mmengine - INFO - Iter(train) [ 81200/120000] base_lr: 4.8834e-05 lr: 6.2576e-06 eta: 1 day, 6:23:24 time: 2.8175 data_time: 0.1601 memory: 14134 grad_norm: 0.6289 loss: 0.4626 detection_loss_cls: 0.4626 2024/07/14 05:20:49 - mmengine - INFO - Iter(train) [ 81250/120000] base_lr: 4.8723e-05 lr: 6.2476e-06 eta: 1 day, 6:21:00 time: 2.8174 data_time: 0.1607 memory: 14134 grad_norm: 0.6292 loss: 0.4636 detection_loss_cls: 0.4636 2024/07/14 05:23:08 - mmengine - INFO - Iter(train) [ 81300/120000] base_lr: 4.8613e-05 lr: 6.2376e-06 eta: 1 day, 6:18:29 time: 2.8170 data_time: 0.1612 memory: 14134 grad_norm: 0.6292 loss: 0.4641 detection_loss_cls: 0.4641 2024/07/14 05:25:27 - mmengine - INFO - Iter(train) [ 81350/120000] base_lr: 4.8504e-05 lr: 6.2276e-06 eta: 1 day, 6:15:56 time: 2.8165 data_time: 0.1610 memory: 14134 grad_norm: 0.6296 loss: 0.4631 detection_loss_cls: 0.4631 2024/07/14 05:27:47 - mmengine - INFO - Iter(train) [ 81400/120000] base_lr: 4.8394e-05 lr: 6.2176e-06 eta: 1 day, 6:13:28 time: 2.8164 data_time: 0.1609 memory: 14134 grad_norm: 0.6295 loss: 0.4630 detection_loss_cls: 0.4630 2024/07/14 05:30:06 - mmengine - INFO - Iter(train) [ 81450/120000] base_lr: 4.8284e-05 lr: 6.2076e-06 eta: 1 day, 6:10:57 time: 2.8161 data_time: 0.1610 memory: 14134 grad_norm: 0.6300 loss: 0.4627 detection_loss_cls: 0.4627 2024/07/14 05:32:26 - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 05:55:42 - mmengine - INFO - Iter(train) [ 82000/120000] base_lr: 4.7083e-05 lr: 6.0984e-06 eta: 1 day, 5:43:46 time: 2.8142 data_time: 0.1629 memory: 14134 grad_norm: 0.6302 loss: 0.4606 detection_loss_cls: 0.4606 2024/07/14 05:55:42 - mmengine - INFO - Saving checkpoint at 82000 iterations 2024/07/14 05:58:09 - mmengine - INFO - Iter(train) [ 82050/120000] base_lr: 4.6974e-05 lr: 6.0886e-06 eta: 1 day, 5:42:01 time: 2.8139 data_time: 0.1634 memory: 14134 grad_norm: 0.6301 loss: 0.4611 detection_loss_cls: 0.4611 2024/07/14 06:00:28 - mmengine - INFO - Iter(train) [ 82100/120000] base_lr: 4.6866e-05 lr: 6.0787e-06 eta: 1 day, 5:39:29 time: 2.8136 data_time: 0.1633 memory: 14134 grad_norm: 0.6303 loss: 0.4603 detection_loss_cls: 0.4603 2024/07/14 06:02:47 - mmengine - INFO - Iter(train) [ 82150/120000] base_lr: 4.6757e-05 lr: 6.0688e-06 eta: 1 day, 5:36:59 time: 2.8131 data_time: 0.1634 memory: 14134 grad_norm: 0.6306 loss: 0.4601 detection_loss_cls: 0.4601 2024/07/14 06:05:06 - 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mmengine - INFO - Iter(train) [ 82950/120000] base_lr: 4.5035e-05 lr: 5.9123e-06 eta: 1 day, 4:57:51 time: 2.8102 data_time: 0.1662 memory: 14134 grad_norm: 0.6315 loss: 0.4611 detection_loss_cls: 0.4611 2024/07/14 06:42:21 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 06:42:21 - mmengine - INFO - Iter(train) [ 83000/120000] base_lr: 4.4928e-05 lr: 5.9025e-06 eta: 1 day, 4:55:26 time: 2.8102 data_time: 0.1662 memory: 14134 grad_norm: 0.6316 loss: 0.4604 detection_loss_cls: 0.4604 2024/07/14 06:42:21 - mmengine - INFO - Saving checkpoint at 83000 iterations 2024/07/14 06:44:48 - mmengine - INFO - Iter(train) [ 83050/120000] base_lr: 4.4821e-05 lr: 5.8928e-06 eta: 1 day, 4:53:35 time: 2.8099 data_time: 0.1664 memory: 14134 grad_norm: 0.6314 loss: 0.4606 detection_loss_cls: 0.4606 2024/07/14 06:47:08 - mmengine - INFO - Iter(train) [ 83100/120000] base_lr: 4.4715e-05 lr: 5.8831e-06 eta: 1 day, 4:51:13 time: 2.8098 data_time: 0.1668 memory: 14134 grad_norm: 0.6314 loss: 0.4609 detection_loss_cls: 0.4609 2024/07/14 06:49:28 - 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mmengine - INFO - Iter(train) [ 83900/120000] base_lr: 4.3021e-05 lr: 5.7292e-06 eta: 1 day, 4:12:58 time: 2.8081 data_time: 0.1715 memory: 14134 grad_norm: 0.6317 loss: 0.4595 detection_loss_cls: 0.4595 2024/07/14 07:26:49 - mmengine - INFO - Iter(train) [ 83950/120000] base_lr: 4.2916e-05 lr: 5.7197e-06 eta: 1 day, 4:10:32 time: 2.8078 data_time: 0.1712 memory: 14134 grad_norm: 0.6318 loss: 0.4585 detection_loss_cls: 0.4585 2024/07/14 07:29:09 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 07:29:09 - mmengine - INFO - Iter(train) [ 84000/120000] base_lr: 4.2811e-05 lr: 5.7101e-06 eta: 1 day, 4:08:07 time: 2.8077 data_time: 0.1711 memory: 14134 grad_norm: 0.6318 loss: 0.4580 detection_loss_cls: 0.4580 2024/07/14 07:29:09 - mmengine - INFO - Saving checkpoint at 84000 iterations 2024/07/14 07:31:35 - mmengine - INFO - Iter(train) [ 84050/120000] base_lr: 4.2707e-05 lr: 5.7006e-06 eta: 1 day, 4:06:09 time: 2.8021 data_time: 0.1659 memory: 14134 grad_norm: 0.6318 loss: 0.4576 detection_loss_cls: 0.4576 2024/07/14 07:33:55 - 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mmengine - INFO - Iter(train) [ 84850/120000] base_lr: 4.1044e-05 lr: 5.5494e-06 eta: 1 day, 3:27:11 time: 2.7998 data_time: 0.1647 memory: 14134 grad_norm: 0.6320 loss: 0.4550 detection_loss_cls: 0.4550 2024/07/14 08:11:03 - mmengine - INFO - Iter(train) [ 84900/120000] base_lr: 4.0941e-05 lr: 5.5400e-06 eta: 1 day, 3:24:48 time: 2.7998 data_time: 0.1649 memory: 14134 grad_norm: 0.6323 loss: 0.4552 detection_loss_cls: 0.4552 2024/07/14 08:13:23 - mmengine - INFO - Iter(train) [ 84950/120000] base_lr: 4.0838e-05 lr: 5.5307e-06 eta: 1 day, 3:22:25 time: 2.8000 data_time: 0.1645 memory: 14134 grad_norm: 0.6325 loss: 0.4544 detection_loss_cls: 0.4544 2024/07/14 08:15:42 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 08:15:42 - mmengine - INFO - Iter(train) [ 85000/120000] base_lr: 4.0735e-05 lr: 5.5213e-06 eta: 1 day, 3:19:59 time: 2.7997 data_time: 0.1643 memory: 14134 grad_norm: 0.6325 loss: 0.4536 detection_loss_cls: 0.4536 2024/07/14 08:15:42 - mmengine - INFO - Saving checkpoint at 85000 iterations 2024/07/14 08:16:11 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:27 time: 0.4116 data_time: 0.0048 memory: 5705 2024/07/14 08:16:31 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:03 time: 0.4116 data_time: 0.0048 memory: 5705 2024/07/14 08:16:52 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:42 time: 0.4116 data_time: 0.0048 memory: 5705 2024/07/14 08:17:12 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:20 time: 0.4115 data_time: 0.0048 memory: 5705 2024/07/14 08:17:32 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:00 time: 0.4114 data_time: 0.0048 memory: 5705 2024/07/14 08:17:53 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:39 time: 0.4114 data_time: 0.0048 memory: 5705 2024/07/14 08:18:13 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:18 time: 0.4114 data_time: 0.0048 memory: 5705 2024/07/14 08:18:34 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:58 time: 0.4114 data_time: 0.0048 memory: 5705 2024/07/14 08:18:54 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:37 time: 0.4112 data_time: 0.0048 memory: 5705 2024/07/14 08:19:15 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:16 time: 0.4111 data_time: 0.0048 memory: 5705 2024/07/14 08:19:35 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4110 data_time: 0.0048 memory: 5705 2024/07/14 08:19:55 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:35 time: 0.4110 data_time: 0.0048 memory: 5705 2024/07/14 08:20:16 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4109 data_time: 0.0048 memory: 5705 2024/07/14 08:20:36 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4109 data_time: 0.0048 memory: 5705 2024/07/14 08:20:57 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4108 data_time: 0.0048 memory: 5705 2024/07/14 08:21:17 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4108 data_time: 0.0048 memory: 5705 2024/07/14 08:21:33 - mmengine - INFO - Evaluating bbox... 2024/07/14 08:21:59 - mmengine - INFO - bbox_mAP_copypaste: 0.458 0.624 0.498 0.268 0.501 0.624 2024/07/14 08:21:59 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4580 coco/bbox_mAP_50: 0.6240 coco/bbox_mAP_75: 0.4980 coco/bbox_mAP_s: 0.2680 coco/bbox_mAP_m: 0.5010 coco/bbox_mAP_l: 0.6240 data_time: 0.0048 time: 0.4092 2024/07/14 08:24:20 - mmengine - INFO - Iter(train) [ 85050/120000] base_lr: 4.0632e-05 lr: 5.5120e-06 eta: 1 day, 3:19:16 time: 2.8049 data_time: 0.1693 memory: 14134 grad_norm: 0.6328 loss: 0.4531 detection_loss_cls: 0.4531 2024/07/14 08:26:41 - mmengine - INFO - Iter(train) [ 85100/120000] base_lr: 4.0529e-05 lr: 5.5027e-06 eta: 1 day, 3:16:59 time: 2.8054 data_time: 0.1693 memory: 14134 grad_norm: 0.6330 loss: 0.4530 detection_loss_cls: 0.4530 2024/07/14 08:29:01 - mmengine - INFO - Iter(train) [ 85150/120000] base_lr: 4.0427e-05 lr: 5.4933e-06 eta: 1 day, 3:14:36 time: 2.8056 data_time: 0.1694 memory: 14134 grad_norm: 0.6334 loss: 0.4529 detection_loss_cls: 0.4529 2024/07/14 08:31:23 - mmengine - INFO - Iter(train) [ 85200/120000] base_lr: 4.0324e-05 lr: 5.4840e-06 eta: 1 day, 3:12:16 time: 2.8059 data_time: 0.1697 memory: 14134 grad_norm: 0.6339 loss: 0.4534 detection_loss_cls: 0.4534 2024/07/14 08:33:43 - mmengine - INFO - Iter(train) [ 85250/120000] base_lr: 4.0222e-05 lr: 5.4747e-06 eta: 1 day, 3:09:55 time: 2.8059 data_time: 0.1693 memory: 14134 grad_norm: 0.6338 loss: 0.4526 detection_loss_cls: 0.4526 2024/07/14 08:36:03 - mmengine - INFO - Iter(train) [ 85300/120000] base_lr: 4.0120e-05 lr: 5.4654e-06 eta: 1 day, 3:07:31 time: 2.8059 data_time: 0.1691 memory: 14134 grad_norm: 0.6341 loss: 0.4521 detection_loss_cls: 0.4521 2024/07/14 08:38:23 - mmengine - INFO - Iter(train) [ 85350/120000] base_lr: 4.0018e-05 lr: 5.4561e-06 eta: 1 day, 3:05:10 time: 2.8064 data_time: 0.1696 memory: 14134 grad_norm: 0.6341 loss: 0.4526 detection_loss_cls: 0.4526 2024/07/14 08:40:45 - mmengine - INFO - Iter(train) [ 85400/120000] base_lr: 3.9915e-05 lr: 5.4469e-06 eta: 1 day, 3:02:52 time: 2.8068 data_time: 0.1701 memory: 14134 grad_norm: 0.6346 loss: 0.4530 detection_loss_cls: 0.4530 2024/07/14 08:43:04 - mmengine - INFO - Iter(train) [ 85450/120000] base_lr: 3.9814e-05 lr: 5.4376e-06 eta: 1 day, 3:00:27 time: 2.8068 data_time: 0.1701 memory: 14134 grad_norm: 0.6340 loss: 0.4531 detection_loss_cls: 0.4531 2024/07/14 08:45:24 - mmengine - INFO - Iter(train) [ 85500/120000] base_lr: 3.9712e-05 lr: 5.4283e-06 eta: 1 day, 2:58:04 time: 2.8069 data_time: 0.1703 memory: 14134 grad_norm: 0.6342 loss: 0.4535 detection_loss_cls: 0.4535 2024/07/14 08:47:45 - mmengine - INFO - Iter(train) [ 85550/120000] base_lr: 3.9610e-05 lr: 5.4191e-06 eta: 1 day, 2:55:41 time: 2.8069 data_time: 0.1703 memory: 14134 grad_norm: 0.6344 loss: 0.4535 detection_loss_cls: 0.4535 2024/07/14 08:50:04 - mmengine - INFO - Iter(train) [ 85600/120000] base_lr: 3.9508e-05 lr: 5.4099e-06 eta: 1 day, 2:53:16 time: 2.8070 data_time: 0.1702 memory: 14134 grad_norm: 0.6342 loss: 0.4531 detection_loss_cls: 0.4531 2024/07/14 08:52:25 - mmengine - INFO - Iter(train) [ 85650/120000] base_lr: 3.9407e-05 lr: 5.4006e-06 eta: 1 day, 2:50:57 time: 2.8074 data_time: 0.1702 memory: 14134 grad_norm: 0.6345 loss: 0.4530 detection_loss_cls: 0.4530 2024/07/14 08:54:45 - mmengine - INFO - Iter(train) [ 85700/120000] base_lr: 3.9305e-05 lr: 5.3914e-06 eta: 1 day, 2:48:35 time: 2.8074 data_time: 0.1705 memory: 14134 grad_norm: 0.6346 loss: 0.4536 detection_loss_cls: 0.4536 2024/07/14 08:57:06 - mmengine - INFO - Iter(train) [ 85750/120000] base_lr: 3.9204e-05 lr: 5.3822e-06 eta: 1 day, 2:46:13 time: 2.8075 data_time: 0.1708 memory: 14134 grad_norm: 0.6347 loss: 0.4541 detection_loss_cls: 0.4541 2024/07/14 08:59:26 - mmengine - INFO - Iter(train) [ 85800/120000] base_lr: 3.9103e-05 lr: 5.3730e-06 eta: 1 day, 2:43:51 time: 2.8073 data_time: 0.1710 memory: 14134 grad_norm: 0.6349 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 09:01:47 - mmengine - INFO - Iter(train) [ 85850/120000] base_lr: 3.9002e-05 lr: 5.3638e-06 eta: 1 day, 2:41:30 time: 2.8077 data_time: 0.1710 memory: 14134 grad_norm: 0.6348 loss: 0.4545 detection_loss_cls: 0.4545 2024/07/14 09:04:07 - mmengine - INFO - Iter(train) [ 85900/120000] base_lr: 3.8901e-05 lr: 5.3546e-06 eta: 1 day, 2:39:09 time: 2.8082 data_time: 0.1711 memory: 14134 grad_norm: 0.6348 loss: 0.4544 detection_loss_cls: 0.4544 2024/07/14 09:06:28 - mmengine - INFO - Iter(train) [ 85950/120000] base_lr: 3.8800e-05 lr: 5.3455e-06 eta: 1 day, 2:36:49 time: 2.8083 data_time: 0.1710 memory: 14134 grad_norm: 0.6350 loss: 0.4545 detection_loss_cls: 0.4545 2024/07/14 09:08:48 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 09:08:48 - mmengine - INFO - Iter(train) [ 86000/120000] base_lr: 3.8699e-05 lr: 5.3363e-06 eta: 1 day, 2:34:26 time: 2.8089 data_time: 0.1714 memory: 14134 grad_norm: 0.6352 loss: 0.4550 detection_loss_cls: 0.4550 2024/07/14 09:08:48 - mmengine - INFO - Saving checkpoint at 86000 iterations 2024/07/14 09:11:16 - mmengine - INFO - Iter(train) [ 86050/120000] base_lr: 3.8599e-05 lr: 5.3271e-06 eta: 1 day, 2:32:26 time: 2.8089 data_time: 0.1714 memory: 14134 grad_norm: 0.6353 loss: 0.4549 detection_loss_cls: 0.4549 2024/07/14 09:13:36 - mmengine - INFO - Iter(train) [ 86100/120000] base_lr: 3.8498e-05 lr: 5.3180e-06 eta: 1 day, 2:30:06 time: 2.8094 data_time: 0.1717 memory: 14134 grad_norm: 0.6350 loss: 0.4552 detection_loss_cls: 0.4552 2024/07/14 09:15:55 - mmengine - INFO - Iter(train) [ 86150/120000] base_lr: 3.8398e-05 lr: 5.3089e-06 eta: 1 day, 2:27:38 time: 2.8092 data_time: 0.1718 memory: 14134 grad_norm: 0.6349 loss: 0.4552 detection_loss_cls: 0.4552 2024/07/14 09:18:16 - mmengine - INFO - Iter(train) [ 86200/120000] base_lr: 3.8297e-05 lr: 5.2998e-06 eta: 1 day, 2:25:18 time: 2.8097 data_time: 0.1718 memory: 14134 grad_norm: 0.6348 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 09:20:36 - mmengine - INFO - Iter(train) [ 86250/120000] base_lr: 3.8197e-05 lr: 5.2906e-06 eta: 1 day, 2:22:56 time: 2.8098 data_time: 0.1717 memory: 14134 grad_norm: 0.6348 loss: 0.4546 detection_loss_cls: 0.4546 2024/07/14 09:22:56 - mmengine - INFO - Iter(train) [ 86300/120000] base_lr: 3.8097e-05 lr: 5.2815e-06 eta: 1 day, 2:20:34 time: 2.8100 data_time: 0.1717 memory: 14134 grad_norm: 0.6351 loss: 0.4543 detection_loss_cls: 0.4543 2024/07/14 09:25:16 - mmengine - INFO - Iter(train) [ 86350/120000] base_lr: 3.7997e-05 lr: 5.2724e-06 eta: 1 day, 2:18:11 time: 2.8101 data_time: 0.1718 memory: 14134 grad_norm: 0.6354 loss: 0.4545 detection_loss_cls: 0.4545 2024/07/14 09:27:35 - mmengine - INFO - Iter(train) [ 86400/120000] base_lr: 3.7897e-05 lr: 5.2634e-06 eta: 1 day, 2:15:45 time: 2.8101 data_time: 0.1717 memory: 14134 grad_norm: 0.6355 loss: 0.4541 detection_loss_cls: 0.4541 2024/07/14 09:29:55 - mmengine - INFO - Iter(train) [ 86450/120000] base_lr: 3.7797e-05 lr: 5.2543e-06 eta: 1 day, 2:13:23 time: 2.8102 data_time: 0.1719 memory: 14134 grad_norm: 0.6355 loss: 0.4545 detection_loss_cls: 0.4545 2024/07/14 09:32:16 - mmengine - INFO - Iter(train) [ 86500/120000] base_lr: 3.7698e-05 lr: 5.2452e-06 eta: 1 day, 2:11:01 time: 2.8104 data_time: 0.1721 memory: 14134 grad_norm: 0.6353 loss: 0.4549 detection_loss_cls: 0.4549 2024/07/14 09:34:36 - mmengine - INFO - Iter(train) [ 86550/120000] base_lr: 3.7598e-05 lr: 5.2362e-06 eta: 1 day, 2:08:39 time: 2.8106 data_time: 0.1721 memory: 14134 grad_norm: 0.6349 loss: 0.4551 detection_loss_cls: 0.4551 2024/07/14 09:36:56 - mmengine - INFO - Iter(train) [ 86600/120000] base_lr: 3.7498e-05 lr: 5.2271e-06 eta: 1 day, 2:06:17 time: 2.8109 data_time: 0.1721 memory: 14134 grad_norm: 0.6349 loss: 0.4549 detection_loss_cls: 0.4549 2024/07/14 09:39:16 - mmengine - INFO - Iter(train) [ 86650/120000] base_lr: 3.7399e-05 lr: 5.2181e-06 eta: 1 day, 2:03:54 time: 2.8113 data_time: 0.1723 memory: 14134 grad_norm: 0.6350 loss: 0.4550 detection_loss_cls: 0.4550 2024/07/14 09:41:36 - mmengine - INFO - Iter(train) [ 86700/120000] base_lr: 3.7300e-05 lr: 5.2091e-06 eta: 1 day, 2:01:31 time: 2.8113 data_time: 0.1725 memory: 14134 grad_norm: 0.6348 loss: 0.4551 detection_loss_cls: 0.4551 2024/07/14 09:43:57 - mmengine - INFO - Iter(train) [ 86750/120000] base_lr: 3.7201e-05 lr: 5.2001e-06 eta: 1 day, 1:59:11 time: 2.8114 data_time: 0.1721 memory: 14134 grad_norm: 0.6349 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 09:46:16 - mmengine - INFO - Iter(train) [ 86800/120000] base_lr: 3.7102e-05 lr: 5.1911e-06 eta: 1 day, 1:56:46 time: 2.8113 data_time: 0.1723 memory: 14134 grad_norm: 0.6348 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 09:48:37 - mmengine - INFO - Iter(train) [ 86850/120000] base_lr: 3.7003e-05 lr: 5.1821e-06 eta: 1 day, 1:54:24 time: 2.8111 data_time: 0.1727 memory: 14134 grad_norm: 0.6351 loss: 0.4551 detection_loss_cls: 0.4551 2024/07/14 09:50:57 - mmengine - INFO - Iter(train) [ 86900/120000] base_lr: 3.6904e-05 lr: 5.1731e-06 eta: 1 day, 1:52:03 time: 2.8113 data_time: 0.1726 memory: 14134 grad_norm: 0.6352 loss: 0.4548 detection_loss_cls: 0.4548 2024/07/14 09:53:19 - mmengine - INFO - Iter(train) [ 86950/120000] base_lr: 3.6805e-05 lr: 5.1641e-06 eta: 1 day, 1:49:46 time: 2.8119 data_time: 0.1727 memory: 14134 grad_norm: 0.6353 loss: 0.4546 detection_loss_cls: 0.4546 2024/07/14 09:55:39 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 09:55:39 - mmengine - INFO - Iter(train) [ 87000/120000] base_lr: 3.6707e-05 lr: 5.1551e-06 eta: 1 day, 1:47:23 time: 2.8119 data_time: 0.1727 memory: 14134 grad_norm: 0.6352 loss: 0.4548 detection_loss_cls: 0.4548 2024/07/14 09:55:39 - mmengine - INFO - Saving checkpoint at 87000 iterations 2024/07/14 09:58:07 - mmengine - INFO - Iter(train) [ 87050/120000] base_lr: 3.6608e-05 lr: 5.1462e-06 eta: 1 day, 1:45:22 time: 2.8120 data_time: 0.1727 memory: 14134 grad_norm: 0.6353 loss: 0.4545 detection_loss_cls: 0.4545 2024/07/14 10:00:27 - mmengine - INFO - Iter(train) [ 87100/120000] base_lr: 3.6510e-05 lr: 5.1372e-06 eta: 1 day, 1:42:59 time: 2.8119 data_time: 0.1725 memory: 14134 grad_norm: 0.6353 loss: 0.4540 detection_loss_cls: 0.4540 2024/07/14 10:02:46 - mmengine - INFO - Iter(train) [ 87150/120000] base_lr: 3.6411e-05 lr: 5.1283e-06 eta: 1 day, 1:40:35 time: 2.8118 data_time: 0.1724 memory: 14134 grad_norm: 0.6354 loss: 0.4536 detection_loss_cls: 0.4536 2024/07/14 10:05:05 - mmengine - INFO - Iter(train) [ 87200/120000] base_lr: 3.6313e-05 lr: 5.1194e-06 eta: 1 day, 1:38:10 time: 2.8117 data_time: 0.1727 memory: 14134 grad_norm: 0.6353 loss: 0.4546 detection_loss_cls: 0.4546 2024/07/14 10:07:24 - mmengine - INFO - Iter(train) [ 87250/120000] base_lr: 3.6215e-05 lr: 5.1105e-06 eta: 1 day, 1:35:45 time: 2.8116 data_time: 0.1722 memory: 14134 grad_norm: 0.6355 loss: 0.4539 detection_loss_cls: 0.4539 2024/07/14 10:09:44 - mmengine - INFO - Iter(train) [ 87300/120000] base_lr: 3.6117e-05 lr: 5.1016e-06 eta: 1 day, 1:33:22 time: 2.8115 data_time: 0.1723 memory: 14134 grad_norm: 0.6357 loss: 0.4541 detection_loss_cls: 0.4541 2024/07/14 10:12:03 - mmengine - INFO - Iter(train) [ 87350/120000] base_lr: 3.6020e-05 lr: 5.0927e-06 eta: 1 day, 1:30:56 time: 2.8114 data_time: 0.1725 memory: 14134 grad_norm: 0.6357 loss: 0.4544 detection_loss_cls: 0.4544 2024/07/14 10:14:23 - mmengine - INFO - Iter(train) [ 87400/120000] base_lr: 3.5922e-05 lr: 5.0838e-06 eta: 1 day, 1:28:34 time: 2.8116 data_time: 0.1723 memory: 14134 grad_norm: 0.6358 loss: 0.4543 detection_loss_cls: 0.4543 2024/07/14 10:16:43 - mmengine - INFO - Iter(train) [ 87450/120000] base_lr: 3.5824e-05 lr: 5.0749e-06 eta: 1 day, 1:26:12 time: 2.8112 data_time: 0.1726 memory: 14134 grad_norm: 0.6356 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 10:19:03 - mmengine - INFO - Iter(train) [ 87500/120000] base_lr: 3.5727e-05 lr: 5.0661e-06 eta: 1 day, 1:23:49 time: 2.8113 data_time: 0.1728 memory: 14134 grad_norm: 0.6359 loss: 0.4549 detection_loss_cls: 0.4549 2024/07/14 10:21:24 - mmengine - INFO - Iter(train) [ 87550/120000] base_lr: 3.5629e-05 lr: 5.0572e-06 eta: 1 day, 1:21:30 time: 2.8115 data_time: 0.1727 memory: 14134 grad_norm: 0.6358 loss: 0.4551 detection_loss_cls: 0.4551 2024/07/14 10:23:44 - mmengine - INFO - Iter(train) [ 87600/120000] base_lr: 3.5532e-05 lr: 5.0484e-06 eta: 1 day, 1:19:07 time: 2.8114 data_time: 0.1726 memory: 14134 grad_norm: 0.6357 loss: 0.4543 detection_loss_cls: 0.4543 2024/07/14 10:26:06 - mmengine - INFO - Iter(train) [ 87650/120000] base_lr: 3.5435e-05 lr: 5.0395e-06 eta: 1 day, 1:16:50 time: 2.8119 data_time: 0.1728 memory: 14134 grad_norm: 0.6363 loss: 0.4545 detection_loss_cls: 0.4545 2024/07/14 10:28:26 - mmengine - INFO - Iter(train) [ 87700/120000] base_lr: 3.5338e-05 lr: 5.0307e-06 eta: 1 day, 1:14:27 time: 2.8116 data_time: 0.1726 memory: 14134 grad_norm: 0.6365 loss: 0.4542 detection_loss_cls: 0.4542 2024/07/14 10:30:46 - mmengine - INFO - Iter(train) [ 87750/120000] base_lr: 3.5241e-05 lr: 5.0219e-06 eta: 1 day, 1:12:04 time: 2.8117 data_time: 0.1727 memory: 14134 grad_norm: 0.6368 loss: 0.4542 detection_loss_cls: 0.4542 2024/07/14 10:33:05 - mmengine - INFO - Iter(train) [ 87800/120000] base_lr: 3.5144e-05 lr: 5.0131e-06 eta: 1 day, 1:09:41 time: 2.8113 data_time: 0.1728 memory: 14134 grad_norm: 0.6372 loss: 0.4542 detection_loss_cls: 0.4542 2024/07/14 10:35:25 - mmengine - INFO - Iter(train) [ 87850/120000] base_lr: 3.5047e-05 lr: 5.0043e-06 eta: 1 day, 1:07:19 time: 2.8112 data_time: 0.1728 memory: 14134 grad_norm: 0.6364 loss: 0.4542 detection_loss_cls: 0.4542 2024/07/14 10:37:44 - mmengine - INFO - Iter(train) [ 87900/120000] base_lr: 3.4951e-05 lr: 4.9955e-06 eta: 1 day, 1:04:54 time: 2.8109 data_time: 0.1726 memory: 14134 grad_norm: 0.6367 loss: 0.4535 detection_loss_cls: 0.4535 2024/07/14 10:40:04 - mmengine - INFO - Iter(train) [ 87950/120000] base_lr: 3.4854e-05 lr: 4.9868e-06 eta: 1 day, 1:02:31 time: 2.8110 data_time: 0.1729 memory: 14134 grad_norm: 0.6371 loss: 0.4540 detection_loss_cls: 0.4540 2024/07/14 10:42:24 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 10:42:24 - mmengine - INFO - Iter(train) [ 88000/120000] base_lr: 3.4758e-05 lr: 4.9780e-06 eta: 1 day, 1:00:09 time: 2.8111 data_time: 0.1730 memory: 14134 grad_norm: 0.6372 loss: 0.4541 detection_loss_cls: 0.4541 2024/07/14 10:42:24 - mmengine - INFO - Saving checkpoint at 88000 iterations 2024/07/14 10:44:53 - mmengine - INFO - Iter(train) [ 88050/120000] base_lr: 3.4662e-05 lr: 4.9692e-06 eta: 1 day, 0:58:08 time: 2.8117 data_time: 0.1735 memory: 14134 grad_norm: 0.6372 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 10:47:14 - mmengine - INFO - Iter(train) [ 88100/120000] base_lr: 3.4566e-05 lr: 4.9605e-06 eta: 1 day, 0:55:48 time: 2.8121 data_time: 0.1734 memory: 14134 grad_norm: 0.6370 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 10:49:34 - mmengine - INFO - Iter(train) [ 88150/120000] base_lr: 3.4470e-05 lr: 4.9518e-06 eta: 1 day, 0:53:26 time: 2.8124 data_time: 0.1734 memory: 14134 grad_norm: 0.6371 loss: 0.4546 detection_loss_cls: 0.4546 2024/07/14 10:51:55 - mmengine - INFO - Iter(train) [ 88200/120000] base_lr: 3.4374e-05 lr: 4.9431e-06 eta: 1 day, 0:51:06 time: 2.8128 data_time: 0.1733 memory: 14134 grad_norm: 0.6375 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 10:54:15 - mmengine - INFO - Iter(train) [ 88250/120000] base_lr: 3.4278e-05 lr: 4.9344e-06 eta: 1 day, 0:48:44 time: 2.8130 data_time: 0.1734 memory: 14134 grad_norm: 0.6374 loss: 0.4549 detection_loss_cls: 0.4549 2024/07/14 10:56:35 - mmengine - INFO - Iter(train) [ 88300/120000] base_lr: 3.4182e-05 lr: 4.9257e-06 eta: 1 day, 0:46:20 time: 2.8131 data_time: 0.1734 memory: 14134 grad_norm: 0.6373 loss: 0.4548 detection_loss_cls: 0.4548 2024/07/14 10:58:55 - mmengine - INFO - Iter(train) [ 88350/120000] base_lr: 3.4087e-05 lr: 4.9170e-06 eta: 1 day, 0:43:59 time: 2.8131 data_time: 0.1732 memory: 14134 grad_norm: 0.6374 loss: 0.4546 detection_loss_cls: 0.4546 2024/07/14 11:01:16 - mmengine - INFO - Iter(train) [ 88400/120000] base_lr: 3.3991e-05 lr: 4.9083e-06 eta: 1 day, 0:41:39 time: 2.8135 data_time: 0.1734 memory: 14134 grad_norm: 0.6372 loss: 0.4548 detection_loss_cls: 0.4548 2024/07/14 11:03:36 - mmengine - INFO - Iter(train) [ 88450/120000] base_lr: 3.3896e-05 lr: 4.8996e-06 eta: 1 day, 0:39:16 time: 2.8137 data_time: 0.1735 memory: 14134 grad_norm: 0.6375 loss: 0.4549 detection_loss_cls: 0.4549 2024/07/14 11:05:57 - mmengine - INFO - Iter(train) [ 88500/120000] base_lr: 3.3801e-05 lr: 4.8910e-06 eta: 1 day, 0:36:56 time: 2.8142 data_time: 0.1740 memory: 14134 grad_norm: 0.6377 loss: 0.4555 detection_loss_cls: 0.4555 2024/07/14 11:08:17 - mmengine - INFO - Iter(train) [ 88550/120000] base_lr: 3.3706e-05 lr: 4.8823e-06 eta: 1 day, 0:34:34 time: 2.8145 data_time: 0.1738 memory: 14134 grad_norm: 0.6379 loss: 0.4551 detection_loss_cls: 0.4551 2024/07/14 11:10:38 - mmengine - INFO - Iter(train) [ 88600/120000] base_lr: 3.3611e-05 lr: 4.8737e-06 eta: 1 day, 0:32:14 time: 2.8150 data_time: 0.1738 memory: 14134 grad_norm: 0.6378 loss: 0.4553 detection_loss_cls: 0.4553 2024/07/14 11:12:59 - mmengine - INFO - Iter(train) [ 88650/120000] base_lr: 3.3516e-05 lr: 4.8651e-06 eta: 1 day, 0:29:54 time: 2.8152 data_time: 0.1735 memory: 14134 grad_norm: 0.6378 loss: 0.4550 detection_loss_cls: 0.4550 2024/07/14 11:15:19 - mmengine - INFO - Iter(train) [ 88700/120000] base_lr: 3.3421e-05 lr: 4.8564e-06 eta: 1 day, 0:27:33 time: 2.8157 data_time: 0.1740 memory: 14134 grad_norm: 0.6380 loss: 0.4560 detection_loss_cls: 0.4560 2024/07/14 11:17:40 - mmengine - INFO - Iter(train) [ 88750/120000] base_lr: 3.3326e-05 lr: 4.8478e-06 eta: 1 day, 0:25:12 time: 2.8162 data_time: 0.1742 memory: 14134 grad_norm: 0.6380 loss: 0.4563 detection_loss_cls: 0.4563 2024/07/14 11:20:00 - mmengine - INFO - Iter(train) [ 88800/120000] base_lr: 3.3232e-05 lr: 4.8392e-06 eta: 1 day, 0:22:51 time: 2.8166 data_time: 0.1744 memory: 14134 grad_norm: 0.6388 loss: 0.4564 detection_loss_cls: 0.4564 2024/07/14 11:22:21 - mmengine - INFO - Iter(train) [ 88850/120000] base_lr: 3.3137e-05 lr: 4.8307e-06 eta: 1 day, 0:20:31 time: 2.8169 data_time: 0.1748 memory: 14134 grad_norm: 0.6391 loss: 0.4568 detection_loss_cls: 0.4568 2024/07/14 11:24:42 - mmengine - INFO - Iter(train) [ 88900/120000] base_lr: 3.3043e-05 lr: 4.8221e-06 eta: 1 day, 0:18:11 time: 2.8172 data_time: 0.1747 memory: 14134 grad_norm: 0.6392 loss: 0.4567 detection_loss_cls: 0.4567 2024/07/14 11:27:03 - mmengine - INFO - Iter(train) [ 88950/120000] base_lr: 3.2949e-05 lr: 4.8135e-06 eta: 1 day, 0:15:50 time: 2.8173 data_time: 0.1750 memory: 14134 grad_norm: 0.6391 loss: 0.4576 detection_loss_cls: 0.4576 2024/07/14 11:29:23 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 11:29:23 - mmengine - INFO - Iter(train) [ 89000/120000] base_lr: 3.2855e-05 lr: 4.8050e-06 eta: 1 day, 0:13:29 time: 2.8177 data_time: 0.1753 memory: 14134 grad_norm: 0.6397 loss: 0.4580 detection_loss_cls: 0.4580 2024/07/14 11:29:23 - mmengine - INFO - Saving checkpoint at 89000 iterations 2024/07/14 11:31:51 - mmengine - INFO - Iter(train) [ 89050/120000] base_lr: 3.2761e-05 lr: 4.7964e-06 eta: 1 day, 0:11:24 time: 2.8125 data_time: 0.1703 memory: 14134 grad_norm: 0.6395 loss: 0.4587 detection_loss_cls: 0.4587 2024/07/14 11:34:11 - mmengine - INFO - Iter(train) [ 89100/120000] base_lr: 3.2667e-05 lr: 4.7879e-06 eta: 1 day, 0:09:03 time: 2.8121 data_time: 0.1704 memory: 14134 grad_norm: 0.6395 loss: 0.4588 detection_loss_cls: 0.4588 2024/07/14 11:36:33 - mmengine - INFO - Iter(train) [ 89150/120000] base_lr: 3.2573e-05 lr: 4.7794e-06 eta: 1 day, 0:06:43 time: 2.8124 data_time: 0.1706 memory: 14134 grad_norm: 0.6392 loss: 0.4592 detection_loss_cls: 0.4592 2024/07/14 11:38:51 - mmengine - INFO - Iter(train) [ 89200/120000] base_lr: 3.2480e-05 lr: 4.7709e-06 eta: 1 day, 0:04:18 time: 2.8119 data_time: 0.1705 memory: 14134 grad_norm: 0.6392 loss: 0.4588 detection_loss_cls: 0.4588 2024/07/14 11:41:12 - mmengine - INFO - Iter(train) [ 89250/120000] base_lr: 3.2386e-05 lr: 4.7624e-06 eta: 1 day, 0:01:58 time: 2.8119 data_time: 0.1704 memory: 14134 grad_norm: 0.6391 loss: 0.4587 detection_loss_cls: 0.4587 2024/07/14 11:43:34 - mmengine - INFO - Iter(train) [ 89300/120000] base_lr: 3.2293e-05 lr: 4.7539e-06 eta: 23:59:38 time: 2.8123 data_time: 0.1703 memory: 14134 grad_norm: 0.6393 loss: 0.4591 detection_loss_cls: 0.4591 2024/07/14 11:45:53 - mmengine - INFO - Iter(train) [ 89350/120000] base_lr: 3.2200e-05 lr: 4.7454e-06 eta: 23:57:14 time: 2.8119 data_time: 0.1698 memory: 14134 grad_norm: 0.6390 loss: 0.4585 detection_loss_cls: 0.4585 2024/07/14 11:48:12 - mmengine - INFO - Iter(train) [ 89400/120000] base_lr: 3.2106e-05 lr: 4.7370e-06 eta: 23:54:52 time: 2.8115 data_time: 0.1694 memory: 14134 grad_norm: 0.6392 loss: 0.4579 detection_loss_cls: 0.4579 2024/07/14 11:50:34 - mmengine - INFO - Iter(train) [ 89450/120000] base_lr: 3.2013e-05 lr: 4.7285e-06 eta: 23:52:32 time: 2.8120 data_time: 0.1694 memory: 14134 grad_norm: 0.6395 loss: 0.4579 detection_loss_cls: 0.4579 2024/07/14 11:52:54 - mmengine - INFO - Iter(train) [ 89500/120000] base_lr: 3.1921e-05 lr: 4.7201e-06 eta: 23:50:10 time: 2.8120 data_time: 0.1695 memory: 14134 grad_norm: 0.6393 loss: 0.4578 detection_loss_cls: 0.4578 2024/07/14 11:55:15 - mmengine - INFO - Iter(train) [ 89550/120000] base_lr: 3.1828e-05 lr: 4.7116e-06 eta: 23:47:51 time: 2.8123 data_time: 0.1692 memory: 14134 grad_norm: 0.6392 loss: 0.4574 detection_loss_cls: 0.4574 2024/07/14 11:57:35 - mmengine - INFO - Iter(train) [ 89600/120000] base_lr: 3.1735e-05 lr: 4.7032e-06 eta: 23:45:29 time: 2.8124 data_time: 0.1694 memory: 14134 grad_norm: 0.6393 loss: 0.4579 detection_loss_cls: 0.4579 2024/07/14 11:59:56 - mmengine - INFO - Iter(train) [ 89650/120000] base_lr: 3.1643e-05 lr: 4.6948e-06 eta: 23:43:08 time: 2.8124 data_time: 0.1694 memory: 14134 grad_norm: 0.6394 loss: 0.4578 detection_loss_cls: 0.4578 2024/07/14 12:02:16 - mmengine - INFO - Iter(train) [ 89700/120000] base_lr: 3.1550e-05 lr: 4.6864e-06 eta: 23:40:47 time: 2.8124 data_time: 0.1691 memory: 14134 grad_norm: 0.6393 loss: 0.4573 detection_loss_cls: 0.4573 2024/07/14 12:04:36 - mmengine - INFO - Iter(train) [ 89750/120000] base_lr: 3.1458e-05 lr: 4.6780e-06 eta: 23:38:25 time: 2.8122 data_time: 0.1691 memory: 14134 grad_norm: 0.6393 loss: 0.4571 detection_loss_cls: 0.4571 2024/07/14 12:06:56 - mmengine - INFO - Iter(train) [ 89800/120000] base_lr: 3.1366e-05 lr: 4.6696e-06 eta: 23:36:02 time: 2.8121 data_time: 0.1689 memory: 14134 grad_norm: 0.6394 loss: 0.4568 detection_loss_cls: 0.4568 2024/07/14 12:09:16 - mmengine - INFO - Iter(train) [ 89850/120000] base_lr: 3.1274e-05 lr: 4.6612e-06 eta: 23:33:41 time: 2.8120 data_time: 0.1691 memory: 14134 grad_norm: 0.6392 loss: 0.4573 detection_loss_cls: 0.4573 2024/07/14 12:11:37 - mmengine - INFO - Iter(train) [ 89900/120000] base_lr: 3.1182e-05 lr: 4.6529e-06 eta: 23:31:20 time: 2.8120 data_time: 0.1693 memory: 14134 grad_norm: 0.6396 loss: 0.4576 detection_loss_cls: 0.4576 2024/07/14 12:13:56 - mmengine - INFO - Iter(train) [ 89950/120000] base_lr: 3.1090e-05 lr: 4.6445e-06 eta: 23:28:57 time: 2.8117 data_time: 0.1692 memory: 14134 grad_norm: 0.6397 loss: 0.4575 detection_loss_cls: 0.4575 2024/07/14 12:16:16 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 12:16:16 - mmengine - INFO - Iter(train) [ 90000/120000] base_lr: 3.0998e-05 lr: 4.6362e-06 eta: 23:26:35 time: 2.8116 data_time: 0.1693 memory: 14134 grad_norm: 0.6393 loss: 0.4573 detection_loss_cls: 0.4573 2024/07/14 12:16:16 - mmengine - INFO - Saving checkpoint at 90000 iterations 2024/07/14 12:16:45 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:22 time: 0.4107 data_time: 0.0048 memory: 5705 2024/07/14 12:17:05 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:01 time: 0.4107 data_time: 0.0049 memory: 5705 2024/07/14 12:17:26 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:42 time: 0.4108 data_time: 0.0049 memory: 5705 2024/07/14 12:17:47 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:21 time: 0.4108 data_time: 0.0049 memory: 5705 2024/07/14 12:18:07 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:00 time: 0.4108 data_time: 0.0049 memory: 5705 2024/07/14 12:18:28 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:39 time: 0.4107 data_time: 0.0049 memory: 5705 2024/07/14 12:18:48 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:18 time: 0.4107 data_time: 0.0049 memory: 5705 2024/07/14 12:19:09 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:58 time: 0.4107 data_time: 0.0049 memory: 5705 2024/07/14 12:19:29 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:37 time: 0.4106 data_time: 0.0049 memory: 5705 2024/07/14 12:19:49 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:16 time: 0.4106 data_time: 0.0049 memory: 5705 2024/07/14 12:20:09 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4105 data_time: 0.0049 memory: 5705 2024/07/14 12:20:30 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:35 time: 0.4105 data_time: 0.0049 memory: 5705 2024/07/14 12:20:50 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4104 data_time: 0.0049 memory: 5705 2024/07/14 12:21:11 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4104 data_time: 0.0049 memory: 5705 2024/07/14 12:21:31 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4102 data_time: 0.0049 memory: 5705 2024/07/14 12:21:52 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4102 data_time: 0.0049 memory: 5705 2024/07/14 12:22:07 - mmengine - INFO - Evaluating bbox... 2024/07/14 12:22:33 - mmengine - INFO - bbox_mAP_copypaste: 0.458 0.625 0.499 0.269 0.500 0.625 2024/07/14 12:22:33 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4580 coco/bbox_mAP_50: 0.6250 coco/bbox_mAP_75: 0.4990 coco/bbox_mAP_s: 0.2690 coco/bbox_mAP_m: 0.5000 coco/bbox_mAP_l: 0.6250 data_time: 0.0049 time: 0.4089 2024/07/14 12:24:52 - mmengine - INFO - Iter(train) [ 90050/120000] base_lr: 3.0907e-05 lr: 4.6279e-06 eta: 23:25:05 time: 2.8164 data_time: 0.1739 memory: 14134 grad_norm: 0.6397 loss: 0.4568 detection_loss_cls: 0.4568 2024/07/14 12:27:12 - mmengine - INFO - Iter(train) [ 90100/120000] base_lr: 3.0815e-05 lr: 4.6196e-06 eta: 23:22:44 time: 2.8163 data_time: 0.1740 memory: 14134 grad_norm: 0.6398 loss: 0.4571 detection_loss_cls: 0.4571 2024/07/14 12:29:32 - mmengine - INFO - Iter(train) [ 90150/120000] base_lr: 3.0724e-05 lr: 4.6113e-06 eta: 23:20:22 time: 2.8167 data_time: 0.1739 memory: 14134 grad_norm: 0.6402 loss: 0.4569 detection_loss_cls: 0.4569 2024/07/14 12:31:52 - mmengine - INFO - Iter(train) [ 90200/120000] base_lr: 3.0633e-05 lr: 4.6030e-06 eta: 23:18:00 time: 2.8165 data_time: 0.1738 memory: 14134 grad_norm: 0.6400 loss: 0.4569 detection_loss_cls: 0.4569 2024/07/14 12:34:13 - mmengine - INFO - Iter(train) [ 90250/120000] base_lr: 3.0542e-05 lr: 4.5947e-06 eta: 23:15:38 time: 2.8165 data_time: 0.1741 memory: 14134 grad_norm: 0.6402 loss: 0.4577 detection_loss_cls: 0.4577 2024/07/14 12:36:33 - mmengine - INFO - Iter(train) [ 90300/120000] base_lr: 3.0451e-05 lr: 4.5864e-06 eta: 23:13:17 time: 2.8166 data_time: 0.1740 memory: 14134 grad_norm: 0.6402 loss: 0.4576 detection_loss_cls: 0.4576 2024/07/14 12:38:52 - mmengine - INFO - Iter(train) [ 90350/120000] base_lr: 3.0360e-05 lr: 4.5782e-06 eta: 23:10:52 time: 2.8162 data_time: 0.1738 memory: 14134 grad_norm: 0.6400 loss: 0.4570 detection_loss_cls: 0.4570 2024/07/14 12:41:12 - 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Saving checkpoint at 92000 iterations 2024/07/14 13:58:29 - mmengine - INFO - Iter(train) [ 92050/120000] base_lr: 2.7344e-05 lr: 4.3040e-06 eta: 21:50:53 time: 2.8162 data_time: 0.1712 memory: 14134 grad_norm: 0.6453 loss: 0.4521 detection_loss_cls: 0.4521 2024/07/14 14:00:49 - mmengine - INFO - Iter(train) [ 92100/120000] base_lr: 2.7257e-05 lr: 4.2961e-06 eta: 21:48:31 time: 2.8160 data_time: 0.1713 memory: 14134 grad_norm: 0.6454 loss: 0.4523 detection_loss_cls: 0.4523 2024/07/14 14:03:08 - mmengine - INFO - Iter(train) [ 92150/120000] base_lr: 2.7171e-05 lr: 4.2883e-06 eta: 21:46:09 time: 2.8159 data_time: 0.1714 memory: 14134 grad_norm: 0.6460 loss: 0.4526 detection_loss_cls: 0.4526 2024/07/14 14:05:29 - mmengine - INFO - Iter(train) [ 92200/120000] base_lr: 2.7085e-05 lr: 4.2804e-06 eta: 21:43:48 time: 2.8159 data_time: 0.1716 memory: 14134 grad_norm: 0.6460 loss: 0.4527 detection_loss_cls: 0.4527 2024/07/14 14:07:51 - mmengine - INFO - Iter(train) [ 92250/120000] base_lr: 2.6998e-05 lr: 4.2726e-06 eta: 21:41:29 time: 2.8163 data_time: 0.1720 memory: 14134 grad_norm: 0.6463 loss: 0.4532 detection_loss_cls: 0.4532 2024/07/14 14:10:11 - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 15:29:39 - mmengine - INFO - Iter(train) [ 94000/120000] base_lr: 2.4064e-05 lr: 4.0058e-06 eta: 20:19:01 time: 2.8129 data_time: 0.1734 memory: 14134 grad_norm: 0.6489 loss: 0.4532 detection_loss_cls: 0.4532 2024/07/14 15:29:39 - mmengine - INFO - Saving checkpoint at 94000 iterations 2024/07/14 15:32:06 - mmengine - INFO - Iter(train) [ 94050/120000] base_lr: 2.3983e-05 lr: 3.9984e-06 eta: 20:16:49 time: 2.8082 data_time: 0.1684 memory: 14134 grad_norm: 0.6491 loss: 0.4528 detection_loss_cls: 0.4528 2024/07/14 15:34:27 - mmengine - INFO - Iter(train) [ 94100/120000] base_lr: 2.3901e-05 lr: 3.9910e-06 eta: 20:14:28 time: 2.8082 data_time: 0.1682 memory: 14134 grad_norm: 0.6494 loss: 0.4524 detection_loss_cls: 0.4524 2024/07/14 15:36:47 - mmengine - INFO - Iter(train) [ 94150/120000] base_lr: 2.3820e-05 lr: 3.9836e-06 eta: 20:12:08 time: 2.8083 data_time: 0.1686 memory: 14134 grad_norm: 0.6492 loss: 0.4533 detection_loss_cls: 0.4533 2024/07/14 15:39:07 - 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mmengine - INFO - Iter(train) [ 94450/120000] base_lr: 2.3336e-05 lr: 3.9396e-06 eta: 19:58:00 time: 2.8090 data_time: 0.1693 memory: 14134 grad_norm: 0.6505 loss: 0.4540 detection_loss_cls: 0.4540 2024/07/14 15:53:08 - mmengine - INFO - Iter(train) [ 94500/120000] base_lr: 2.3255e-05 lr: 3.9323e-06 eta: 19:55:38 time: 2.8087 data_time: 0.1691 memory: 14134 grad_norm: 0.6504 loss: 0.4537 detection_loss_cls: 0.4537 2024/07/14 15:55:30 - mmengine - INFO - Iter(train) [ 94550/120000] base_lr: 2.3175e-05 lr: 3.9250e-06 eta: 19:53:19 time: 2.8090 data_time: 0.1696 memory: 14134 grad_norm: 0.6508 loss: 0.4546 detection_loss_cls: 0.4546 2024/07/14 15:57:51 - mmengine - INFO - Iter(train) [ 94600/120000] base_lr: 2.3095e-05 lr: 3.9177e-06 eta: 19:50:58 time: 2.8090 data_time: 0.1695 memory: 14134 grad_norm: 0.6503 loss: 0.4544 detection_loss_cls: 0.4544 2024/07/14 16:00:12 - mmengine - INFO - Iter(train) [ 94650/120000] base_lr: 2.3015e-05 lr: 3.9105e-06 eta: 19:48:38 time: 2.8092 data_time: 0.1696 memory: 14134 grad_norm: 0.6502 loss: 0.4545 detection_loss_cls: 0.4545 2024/07/14 16:02:32 - mmengine - INFO - Iter(train) [ 94700/120000] base_lr: 2.2935e-05 lr: 3.9032e-06 eta: 19:46:16 time: 2.8094 data_time: 0.1697 memory: 14134 grad_norm: 0.6500 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 16:04:52 - mmengine - INFO - Iter(train) [ 94750/120000] base_lr: 2.2856e-05 lr: 3.8960e-06 eta: 19:43:55 time: 2.8092 data_time: 0.1701 memory: 14134 grad_norm: 0.6502 loss: 0.4557 detection_loss_cls: 0.4557 2024/07/14 16:07:12 - mmengine - INFO - Iter(train) [ 94800/120000] base_lr: 2.2776e-05 lr: 3.8887e-06 eta: 19:41:34 time: 2.8092 data_time: 0.1700 memory: 14134 grad_norm: 0.6501 loss: 0.4553 detection_loss_cls: 0.4553 2024/07/14 16:09:32 - mmengine - INFO - Iter(train) [ 94850/120000] base_lr: 2.2697e-05 lr: 3.8815e-06 eta: 19:39:12 time: 2.8092 data_time: 0.1701 memory: 14134 grad_norm: 0.6497 loss: 0.4555 detection_loss_cls: 0.4555 2024/07/14 16:11:52 - mmengine - INFO - Iter(train) [ 94900/120000] base_lr: 2.2618e-05 lr: 3.8743e-06 eta: 19:36:50 time: 2.8092 data_time: 0.1700 memory: 14134 grad_norm: 0.6494 loss: 0.4549 detection_loss_cls: 0.4549 2024/07/14 16:14:12 - mmengine - INFO - Iter(train) [ 94950/120000] base_lr: 2.2539e-05 lr: 3.8671e-06 eta: 19:34:29 time: 2.8089 data_time: 0.1700 memory: 14134 grad_norm: 0.6493 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 16:16:31 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 16:16:31 - mmengine - INFO - Iter(train) [ 95000/120000] base_lr: 2.2460e-05 lr: 3.8600e-06 eta: 19:32:06 time: 2.8086 data_time: 0.1698 memory: 14134 grad_norm: 0.6492 loss: 0.4542 detection_loss_cls: 0.4542 2024/07/14 16:16:31 - mmengine - INFO - Saving checkpoint at 95000 iterations 2024/07/14 16:17:00 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:23 time: 0.4100 data_time: 0.0049 memory: 5705 2024/07/14 16:17:20 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:01 time: 0.4099 data_time: 0.0049 memory: 5705 2024/07/14 16:17:41 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:41 time: 0.4099 data_time: 0.0049 memory: 5705 2024/07/14 16:18:01 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:20 time: 0.4099 data_time: 0.0049 memory: 5705 2024/07/14 16:18:22 - mmengine - INFO - Iter(val) [250/834] eta: 0:03:59 time: 0.4098 data_time: 0.0049 memory: 5705 2024/07/14 16:18:42 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:38 time: 0.4097 data_time: 0.0049 memory: 5705 2024/07/14 16:19:02 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:18 time: 0.4097 data_time: 0.0049 memory: 5705 2024/07/14 16:19:23 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:57 time: 0.4097 data_time: 0.0049 memory: 5705 2024/07/14 16:19:43 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:37 time: 0.4096 data_time: 0.0049 memory: 5705 2024/07/14 16:20:04 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:16 time: 0.4094 data_time: 0.0049 memory: 5705 2024/07/14 16:20:24 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4093 data_time: 0.0049 memory: 5705 2024/07/14 16:20:45 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:35 time: 0.4093 data_time: 0.0049 memory: 5705 2024/07/14 16:21:05 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4092 data_time: 0.0049 memory: 5705 2024/07/14 16:21:26 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4091 data_time: 0.0048 memory: 5705 2024/07/14 16:21:46 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/14 16:22:06 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/14 16:22:22 - mmengine - INFO - Evaluating bbox... 2024/07/14 16:22:48 - mmengine - INFO - bbox_mAP_copypaste: 0.458 0.625 0.498 0.268 0.500 0.627 2024/07/14 16:22:48 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4580 coco/bbox_mAP_50: 0.6250 coco/bbox_mAP_75: 0.4980 coco/bbox_mAP_s: 0.2680 coco/bbox_mAP_m: 0.5000 coco/bbox_mAP_l: 0.6270 data_time: 0.0048 time: 0.4089 2024/07/14 16:25:08 - mmengine - INFO - Iter(train) [ 95050/120000] base_lr: 2.2381e-05 lr: 3.8528e-06 eta: 19:30:19 time: 2.8134 data_time: 0.1752 memory: 14134 grad_norm: 0.6492 loss: 0.4544 detection_loss_cls: 0.4544 2024/07/14 16:27:28 - mmengine - INFO - Iter(train) [ 95100/120000] base_lr: 2.2302e-05 lr: 3.8456e-06 eta: 19:27:58 time: 2.8134 data_time: 0.1753 memory: 14134 grad_norm: 0.6493 loss: 0.4545 detection_loss_cls: 0.4545 2024/07/14 16:29:49 - mmengine - INFO - Iter(train) [ 95150/120000] base_lr: 2.2224e-05 lr: 3.8385e-06 eta: 19:25:38 time: 2.8136 data_time: 0.1756 memory: 14134 grad_norm: 0.6495 loss: 0.4549 detection_loss_cls: 0.4549 2024/07/14 16:32:11 - mmengine - INFO - Iter(train) [ 95200/120000] base_lr: 2.2145e-05 lr: 3.8314e-06 eta: 19:23:18 time: 2.8143 data_time: 0.1758 memory: 14134 grad_norm: 0.6493 loss: 0.4549 detection_loss_cls: 0.4549 2024/07/14 16:34:31 - mmengine - INFO - Iter(train) [ 95250/120000] base_lr: 2.2067e-05 lr: 3.8243e-06 eta: 19:20:57 time: 2.8146 data_time: 0.1761 memory: 14134 grad_norm: 0.6495 loss: 0.4551 detection_loss_cls: 0.4551 2024/07/14 16:36:51 - mmengine - INFO - Iter(train) [ 95300/120000] base_lr: 2.1989e-05 lr: 3.8172e-06 eta: 19:18:35 time: 2.8144 data_time: 0.1762 memory: 14134 grad_norm: 0.6497 loss: 0.4554 detection_loss_cls: 0.4554 2024/07/14 16:39:11 - mmengine - INFO - Iter(train) [ 95350/120000] base_lr: 2.1911e-05 lr: 3.8101e-06 eta: 19:16:14 time: 2.8145 data_time: 0.1763 memory: 14134 grad_norm: 0.6490 loss: 0.4556 detection_loss_cls: 0.4556 2024/07/14 16:41:32 - mmengine - INFO - Iter(train) [ 95400/120000] base_lr: 2.1833e-05 lr: 3.8030e-06 eta: 19:13:53 time: 2.8151 data_time: 0.1768 memory: 14134 grad_norm: 0.6491 loss: 0.4563 detection_loss_cls: 0.4563 2024/07/14 16:43:53 - mmengine - INFO - Iter(train) [ 95450/120000] base_lr: 2.1755e-05 lr: 3.7959e-06 eta: 19:11:33 time: 2.8157 data_time: 0.1768 memory: 14134 grad_norm: 0.6490 loss: 0.4559 detection_loss_cls: 0.4559 2024/07/14 16:46:12 - mmengine - INFO - Iter(train) [ 95500/120000] base_lr: 2.1677e-05 lr: 3.7889e-06 eta: 19:09:10 time: 2.8155 data_time: 0.1765 memory: 14134 grad_norm: 0.6492 loss: 0.4557 detection_loss_cls: 0.4557 2024/07/14 16:48:32 - mmengine - INFO - Iter(train) [ 95550/120000] base_lr: 2.1600e-05 lr: 3.7818e-06 eta: 19:06:48 time: 2.8153 data_time: 0.1762 memory: 14134 grad_norm: 0.6488 loss: 0.4555 detection_loss_cls: 0.4555 2024/07/14 16:50:52 - mmengine - INFO - Iter(train) [ 95600/120000] base_lr: 2.1523e-05 lr: 3.7748e-06 eta: 19:04:27 time: 2.8152 data_time: 0.1765 memory: 14134 grad_norm: 0.6486 loss: 0.4559 detection_loss_cls: 0.4559 2024/07/14 16:53:13 - mmengine - INFO - Iter(train) [ 95650/120000] base_lr: 2.1445e-05 lr: 3.7678e-06 eta: 19:02:06 time: 2.8156 data_time: 0.1765 memory: 14134 grad_norm: 0.6492 loss: 0.4557 detection_loss_cls: 0.4557 2024/07/14 16:55:33 - mmengine - INFO - Iter(train) [ 95700/120000] base_lr: 2.1368e-05 lr: 3.7608e-06 eta: 18:59:45 time: 2.8154 data_time: 0.1763 memory: 14134 grad_norm: 0.6495 loss: 0.4555 detection_loss_cls: 0.4555 2024/07/14 16:57:54 - mmengine - INFO - Iter(train) [ 95750/120000] base_lr: 2.1291e-05 lr: 3.7538e-06 eta: 18:57:25 time: 2.8157 data_time: 0.1766 memory: 14134 grad_norm: 0.6503 loss: 0.4558 detection_loss_cls: 0.4558 2024/07/14 17:00:15 - mmengine - INFO - Iter(train) [ 95800/120000] base_lr: 2.1215e-05 lr: 3.7468e-06 eta: 18:55:04 time: 2.8161 data_time: 0.1772 memory: 14134 grad_norm: 0.6506 loss: 0.4566 detection_loss_cls: 0.4566 2024/07/14 17:02:37 - mmengine - INFO - Iter(train) [ 95850/120000] base_lr: 2.1138e-05 lr: 3.7398e-06 eta: 18:52:44 time: 2.8165 data_time: 0.1773 memory: 14134 grad_norm: 0.6514 loss: 0.4568 detection_loss_cls: 0.4568 2024/07/14 17:04:58 - mmengine - INFO - Iter(train) [ 95900/120000] base_lr: 2.1061e-05 lr: 3.7329e-06 eta: 18:50:24 time: 2.8167 data_time: 0.1773 memory: 14134 grad_norm: 0.6518 loss: 0.4565 detection_loss_cls: 0.4565 2024/07/14 17:07:18 - mmengine - INFO - Iter(train) [ 95950/120000] base_lr: 2.0985e-05 lr: 3.7259e-06 eta: 18:48:03 time: 2.8169 data_time: 0.1775 memory: 14134 grad_norm: 0.6519 loss: 0.4565 detection_loss_cls: 0.4565 2024/07/14 17:09:39 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 17:09:39 - mmengine - INFO - Iter(train) [ 96000/120000] base_lr: 2.0909e-05 lr: 3.7190e-06 eta: 18:45:42 time: 2.8171 data_time: 0.1777 memory: 14134 grad_norm: 0.6519 loss: 0.4566 detection_loss_cls: 0.4566 2024/07/14 17:09:39 - mmengine - INFO - Saving checkpoint at 96000 iterations 2024/07/14 17:12:08 - mmengine - INFO - Iter(train) [ 96050/120000] base_lr: 2.0833e-05 lr: 3.7121e-06 eta: 18:43:31 time: 2.8171 data_time: 0.1776 memory: 14134 grad_norm: 0.6525 loss: 0.4563 detection_loss_cls: 0.4563 2024/07/14 17:14:27 - mmengine - INFO - Iter(train) [ 96100/120000] base_lr: 2.0757e-05 lr: 3.7052e-06 eta: 18:41:08 time: 2.8169 data_time: 0.1774 memory: 14134 grad_norm: 0.6527 loss: 0.4558 detection_loss_cls: 0.4558 2024/07/14 17:16:46 - mmengine - INFO - Iter(train) [ 96150/120000] base_lr: 2.0681e-05 lr: 3.6983e-06 eta: 18:38:45 time: 2.8168 data_time: 0.1775 memory: 14134 grad_norm: 0.6521 loss: 0.4557 detection_loss_cls: 0.4557 2024/07/14 17:19:06 - mmengine - INFO - Iter(train) [ 96200/120000] base_lr: 2.0605e-05 lr: 3.6914e-06 eta: 18:36:24 time: 2.8165 data_time: 0.1772 memory: 14134 grad_norm: 0.6521 loss: 0.4552 detection_loss_cls: 0.4552 2024/07/14 17:21:25 - mmengine - INFO - Iter(train) [ 96250/120000] base_lr: 2.0530e-05 lr: 3.6845e-06 eta: 18:34:02 time: 2.8160 data_time: 0.1768 memory: 14134 grad_norm: 0.6519 loss: 0.4543 detection_loss_cls: 0.4543 2024/07/14 17:23:45 - mmengine - INFO - Iter(train) [ 96300/120000] base_lr: 2.0454e-05 lr: 3.6777e-06 eta: 18:31:40 time: 2.8158 data_time: 0.1771 memory: 14134 grad_norm: 0.6520 loss: 0.4547 detection_loss_cls: 0.4547 2024/07/14 17:26:04 - mmengine - INFO - Iter(train) [ 96350/120000] base_lr: 2.0379e-05 lr: 3.6708e-06 eta: 18:29:17 time: 2.8158 data_time: 0.1773 memory: 14134 grad_norm: 0.6523 loss: 0.4550 detection_loss_cls: 0.4550 2024/07/14 17:28:24 - mmengine - INFO - Iter(train) [ 96400/120000] base_lr: 2.0304e-05 lr: 3.6640e-06 eta: 18:26:56 time: 2.8157 data_time: 0.1777 memory: 14134 grad_norm: 0.6523 loss: 0.4556 detection_loss_cls: 0.4556 2024/07/14 17:30:45 - mmengine - INFO - Iter(train) [ 96450/120000] base_lr: 2.0229e-05 lr: 3.6572e-06 eta: 18:24:35 time: 2.8156 data_time: 0.1775 memory: 14134 grad_norm: 0.6524 loss: 0.4553 detection_loss_cls: 0.4553 2024/07/14 17:33:04 - mmengine - INFO - Iter(train) [ 96500/120000] base_lr: 2.0154e-05 lr: 3.6504e-06 eta: 18:22:12 time: 2.8157 data_time: 0.1776 memory: 14134 grad_norm: 0.6521 loss: 0.4556 detection_loss_cls: 0.4556 2024/07/14 17:35:25 - mmengine - INFO - Iter(train) [ 96550/120000] base_lr: 2.0079e-05 lr: 3.6436e-06 eta: 18:19:52 time: 2.8158 data_time: 0.1776 memory: 14134 grad_norm: 0.6524 loss: 0.4556 detection_loss_cls: 0.4556 2024/07/14 17:37:46 - 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Saving checkpoint at 97000 iterations 2024/07/14 17:58:55 - mmengine - INFO - Iter(train) [ 97050/120000] base_lr: 1.9340e-05 lr: 3.5763e-06 eta: 17:56:28 time: 2.8162 data_time: 0.1769 memory: 14134 grad_norm: 0.6536 loss: 0.4529 detection_loss_cls: 0.4529 2024/07/14 18:01:17 - mmengine - INFO - Iter(train) [ 97100/120000] base_lr: 1.9266e-05 lr: 3.5697e-06 eta: 17:54:08 time: 2.8168 data_time: 0.1767 memory: 14134 grad_norm: 0.6534 loss: 0.4522 detection_loss_cls: 0.4522 2024/07/14 18:03:38 - mmengine - INFO - Iter(train) [ 97150/120000] base_lr: 1.9193e-05 lr: 3.5630e-06 eta: 17:51:48 time: 2.8170 data_time: 0.1771 memory: 14134 grad_norm: 0.6535 loss: 0.4527 detection_loss_cls: 0.4527 2024/07/14 18:05:59 - mmengine - INFO - Iter(train) [ 97200/120000] base_lr: 1.9120e-05 lr: 3.5564e-06 eta: 17:49:27 time: 2.8173 data_time: 0.1773 memory: 14134 grad_norm: 0.6539 loss: 0.4528 detection_loss_cls: 0.4528 2024/07/14 18:08:18 - mmengine - INFO - Iter(train) [ 97250/120000] base_lr: 1.9048e-05 lr: 3.5498e-06 eta: 17:47:05 time: 2.8171 data_time: 0.1771 memory: 14134 grad_norm: 0.6541 loss: 0.4526 detection_loss_cls: 0.4526 2024/07/14 18:10:38 - 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mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 19:30:35 - mmengine - INFO - Iter(train) [ 99000/120000] base_lr: 1.6590e-05 lr: 3.3264e-06 eta: 16:25:10 time: 2.8233 data_time: 0.1794 memory: 14134 grad_norm: 0.6574 loss: 0.4506 detection_loss_cls: 0.4506 2024/07/14 19:30:35 - mmengine - INFO - Saving checkpoint at 99000 iterations 2024/07/14 19:33:04 - mmengine - INFO - Iter(train) [ 99050/120000] base_lr: 1.6522e-05 lr: 3.3202e-06 eta: 16:22:56 time: 2.8186 data_time: 0.1745 memory: 14134 grad_norm: 0.6575 loss: 0.4514 detection_loss_cls: 0.4514 2024/07/14 19:35:25 - mmengine - INFO - Iter(train) [ 99100/120000] base_lr: 1.6455e-05 lr: 3.3141e-06 eta: 16:20:35 time: 2.8188 data_time: 0.1746 memory: 14134 grad_norm: 0.6574 loss: 0.4513 detection_loss_cls: 0.4513 2024/07/14 19:37:45 - mmengine - INFO - Iter(train) [ 99150/120000] base_lr: 1.6387e-05 lr: 3.3080e-06 eta: 16:18:14 time: 2.8185 data_time: 0.1747 memory: 14134 grad_norm: 0.6574 loss: 0.4513 detection_loss_cls: 0.4513 2024/07/14 19:40:06 - 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mmengine - INFO - Iter(train) [ 99950/120000] base_lr: 1.5330e-05 lr: 3.2118e-06 eta: 15:40:35 time: 2.8171 data_time: 0.1761 memory: 14134 grad_norm: 0.6584 loss: 0.4528 detection_loss_cls: 0.4528 2024/07/14 20:17:29 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 20:17:29 - mmengine - INFO - Iter(train) [100000/120000] base_lr: 1.5265e-05 lr: 3.2059e-06 eta: 15:38:14 time: 2.8170 data_time: 0.1761 memory: 14134 grad_norm: 0.6585 loss: 0.4526 detection_loss_cls: 0.4526 2024/07/14 20:17:29 - mmengine - INFO - Saving checkpoint at 100000 iterations 2024/07/14 20:17:57 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:26 time: 0.4089 data_time: 0.0048 memory: 5705 2024/07/14 20:18:18 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:03 time: 0.4089 data_time: 0.0049 memory: 5705 2024/07/14 20:18:39 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:43 time: 0.4091 data_time: 0.0049 memory: 5705 2024/07/14 20:18:59 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:22 time: 0.4090 data_time: 0.0049 memory: 5705 2024/07/14 20:19:20 - 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mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4086 data_time: 0.0048 memory: 5705 2024/07/14 20:22:44 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4086 data_time: 0.0048 memory: 5705 2024/07/14 20:23:04 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4086 data_time: 0.0048 memory: 5705 2024/07/14 20:23:20 - mmengine - INFO - Evaluating bbox... 2024/07/14 20:23:45 - mmengine - INFO - bbox_mAP_copypaste: 0.458 0.625 0.498 0.266 0.500 0.628 2024/07/14 20:23:45 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4580 coco/bbox_mAP_50: 0.6250 coco/bbox_mAP_75: 0.4980 coco/bbox_mAP_s: 0.2660 coco/bbox_mAP_m: 0.5000 coco/bbox_mAP_l: 0.6280 data_time: 0.0049 time: 0.4093 2024/07/14 20:26:05 - mmengine - INFO - Iter(train) [100050/120000] base_lr: 1.5200e-05 lr: 3.2000e-06 eta: 15:36:15 time: 2.8218 data_time: 0.1812 memory: 14134 grad_norm: 0.6581 loss: 0.4525 detection_loss_cls: 0.4525 2024/07/14 20:28:26 - mmengine - INFO - Iter(train) [100100/120000] base_lr: 1.5135e-05 lr: 3.1941e-06 eta: 15:33:54 time: 2.8221 data_time: 0.1813 memory: 14134 grad_norm: 0.6579 loss: 0.4527 detection_loss_cls: 0.4527 2024/07/14 20:30:47 - mmengine - INFO - Iter(train) [100150/120000] base_lr: 1.5071e-05 lr: 3.1883e-06 eta: 15:31:33 time: 2.8226 data_time: 0.1812 memory: 14134 grad_norm: 0.6581 loss: 0.4527 detection_loss_cls: 0.4527 2024/07/14 20:33:07 - mmengine - INFO - Iter(train) [100200/120000] base_lr: 1.5007e-05 lr: 3.1824e-06 eta: 15:29:12 time: 2.8226 data_time: 0.1812 memory: 14134 grad_norm: 0.6582 loss: 0.4527 detection_loss_cls: 0.4527 2024/07/14 20:35:28 - mmengine - INFO - Iter(train) [100250/120000] base_lr: 1.4943e-05 lr: 3.1766e-06 eta: 15:26:51 time: 2.8231 data_time: 0.1814 memory: 14134 grad_norm: 0.6585 loss: 0.4528 detection_loss_cls: 0.4528 2024/07/14 20:37:49 - mmengine - INFO - Iter(train) [100300/120000] base_lr: 1.4879e-05 lr: 3.1708e-06 eta: 15:24:30 time: 2.8232 data_time: 0.1815 memory: 14134 grad_norm: 0.6586 loss: 0.4531 detection_loss_cls: 0.4531 2024/07/14 20:40:08 - mmengine - INFO - Iter(train) [100350/120000] base_lr: 1.4815e-05 lr: 3.1650e-06 eta: 15:22:08 time: 2.8233 data_time: 0.1814 memory: 14134 grad_norm: 0.6585 loss: 0.4532 detection_loss_cls: 0.4532 2024/07/14 20:42:29 - mmengine - INFO - Iter(train) [100400/120000] base_lr: 1.4751e-05 lr: 3.1592e-06 eta: 15:19:47 time: 2.8235 data_time: 0.1811 memory: 14134 grad_norm: 0.6586 loss: 0.4524 detection_loss_cls: 0.4524 2024/07/14 20:44:49 - mmengine - INFO - Iter(train) [100450/120000] base_lr: 1.4688e-05 lr: 3.1534e-06 eta: 15:17:26 time: 2.8234 data_time: 0.1813 memory: 14134 grad_norm: 0.6586 loss: 0.4523 detection_loss_cls: 0.4523 2024/07/14 20:47:09 - mmengine - INFO - Iter(train) [100500/120000] base_lr: 1.4624e-05 lr: 3.1477e-06 eta: 15:15:04 time: 2.8235 data_time: 0.1813 memory: 14134 grad_norm: 0.6585 loss: 0.4519 detection_loss_cls: 0.4519 2024/07/14 20:49:29 - mmengine - INFO - Iter(train) [100550/120000] base_lr: 1.4561e-05 lr: 3.1419e-06 eta: 15:12:43 time: 2.8233 data_time: 0.1812 memory: 14134 grad_norm: 0.6588 loss: 0.4513 detection_loss_cls: 0.4513 2024/07/14 20:51:51 - mmengine - INFO - Iter(train) [100600/120000] base_lr: 1.4498e-05 lr: 3.1362e-06 eta: 15:10:23 time: 2.8235 data_time: 0.1813 memory: 14134 grad_norm: 0.6583 loss: 0.4518 detection_loss_cls: 0.4518 2024/07/14 20:54:11 - mmengine - INFO - Iter(train) [100650/120000] base_lr: 1.4435e-05 lr: 3.1304e-06 eta: 15:08:02 time: 2.8236 data_time: 0.1811 memory: 14134 grad_norm: 0.6581 loss: 0.4517 detection_loss_cls: 0.4517 2024/07/14 20:56:32 - mmengine - INFO - Iter(train) [100700/120000] base_lr: 1.4372e-05 lr: 3.1247e-06 eta: 15:05:41 time: 2.8237 data_time: 0.1814 memory: 14134 grad_norm: 0.6583 loss: 0.4522 detection_loss_cls: 0.4522 2024/07/14 20:58:52 - mmengine - INFO - Iter(train) [100750/120000] base_lr: 1.4309e-05 lr: 3.1190e-06 eta: 15:03:20 time: 2.8237 data_time: 0.1814 memory: 14134 grad_norm: 0.6583 loss: 0.4524 detection_loss_cls: 0.4524 2024/07/14 21:01:12 - mmengine - INFO - Iter(train) [100800/120000] base_lr: 1.4247e-05 lr: 3.1134e-06 eta: 15:00:59 time: 2.8239 data_time: 0.1810 memory: 14134 grad_norm: 0.6580 loss: 0.4520 detection_loss_cls: 0.4520 2024/07/14 21:03:33 - mmengine - INFO - Iter(train) [100850/120000] base_lr: 1.4185e-05 lr: 3.1077e-06 eta: 14:58:38 time: 2.8239 data_time: 0.1812 memory: 14134 grad_norm: 0.6576 loss: 0.4521 detection_loss_cls: 0.4521 2024/07/14 21:05:53 - mmengine - INFO - Iter(train) [100900/120000] base_lr: 1.4122e-05 lr: 3.1020e-06 eta: 14:56:17 time: 2.8241 data_time: 0.1813 memory: 14134 grad_norm: 0.6576 loss: 0.4519 detection_loss_cls: 0.4519 2024/07/14 21:08:14 - mmengine - INFO - Iter(train) [100950/120000] base_lr: 1.4060e-05 lr: 3.0964e-06 eta: 14:53:56 time: 2.8244 data_time: 0.1815 memory: 14134 grad_norm: 0.6574 loss: 0.4522 detection_loss_cls: 0.4522 2024/07/14 21:10:35 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 21:10:35 - mmengine - INFO - Iter(train) [101000/120000] base_lr: 1.3998e-05 lr: 3.0908e-06 eta: 14:51:35 time: 2.8244 data_time: 0.1816 memory: 14134 grad_norm: 0.6574 loss: 0.4525 detection_loss_cls: 0.4525 2024/07/14 21:10:35 - mmengine - INFO - Saving checkpoint at 101000 iterations 2024/07/14 21:13:04 - mmengine - INFO - Iter(train) [101050/120000] base_lr: 1.3937e-05 lr: 3.0851e-06 eta: 14:49:20 time: 2.8245 data_time: 0.1817 memory: 14134 grad_norm: 0.6577 loss: 0.4527 detection_loss_cls: 0.4527 2024/07/14 21:15:25 - mmengine - INFO - Iter(train) [101100/120000] base_lr: 1.3875e-05 lr: 3.0795e-06 eta: 14:47:00 time: 2.8243 data_time: 0.1823 memory: 14134 grad_norm: 0.6581 loss: 0.4536 detection_loss_cls: 0.4536 2024/07/14 21:17:45 - mmengine - INFO - Iter(train) [101150/120000] base_lr: 1.3813e-05 lr: 3.0740e-06 eta: 14:44:38 time: 2.8239 data_time: 0.1821 memory: 14134 grad_norm: 0.6578 loss: 0.4530 detection_loss_cls: 0.4530 2024/07/14 21:20:06 - mmengine - INFO - Iter(train) [101200/120000] base_lr: 1.3752e-05 lr: 3.0684e-06 eta: 14:42:18 time: 2.8241 data_time: 0.1821 memory: 14134 grad_norm: 0.6576 loss: 0.4528 detection_loss_cls: 0.4528 2024/07/14 21:22:25 - mmengine - INFO - Iter(train) [101250/120000] base_lr: 1.3691e-05 lr: 3.0628e-06 eta: 14:39:56 time: 2.8242 data_time: 0.1821 memory: 14134 grad_norm: 0.6575 loss: 0.4522 detection_loss_cls: 0.4522 2024/07/14 21:24:45 - mmengine - INFO - Iter(train) [101300/120000] base_lr: 1.3630e-05 lr: 3.0573e-06 eta: 14:37:35 time: 2.8240 data_time: 0.1821 memory: 14134 grad_norm: 0.6572 loss: 0.4518 detection_loss_cls: 0.4518 2024/07/14 21:27:05 - mmengine - INFO - Iter(train) [101350/120000] base_lr: 1.3569e-05 lr: 3.0517e-06 eta: 14:35:13 time: 2.8236 data_time: 0.1823 memory: 14134 grad_norm: 0.6570 loss: 0.4523 detection_loss_cls: 0.4523 2024/07/14 21:29:26 - mmengine - INFO - Iter(train) [101400/120000] base_lr: 1.3508e-05 lr: 3.0462e-06 eta: 14:32:52 time: 2.8235 data_time: 0.1826 memory: 14134 grad_norm: 0.6569 loss: 0.4526 detection_loss_cls: 0.4526 2024/07/14 21:31:46 - mmengine - INFO - Iter(train) [101450/120000] base_lr: 1.3448e-05 lr: 3.0407e-06 eta: 14:30:31 time: 2.8231 data_time: 0.1825 memory: 14134 grad_norm: 0.6568 loss: 0.4527 detection_loss_cls: 0.4527 2024/07/14 21:34:06 - mmengine - INFO - Iter(train) [101500/120000] base_lr: 1.3387e-05 lr: 3.0352e-06 eta: 14:28:09 time: 2.8227 data_time: 0.1825 memory: 14134 grad_norm: 0.6570 loss: 0.4527 detection_loss_cls: 0.4527 2024/07/14 21:36:25 - mmengine - INFO - Iter(train) [101550/120000] base_lr: 1.3327e-05 lr: 3.0297e-06 eta: 14:25:48 time: 2.8225 data_time: 0.1826 memory: 14134 grad_norm: 0.6568 loss: 0.4531 detection_loss_cls: 0.4531 2024/07/14 21:38:46 - mmengine - INFO - Iter(train) [101600/120000] base_lr: 1.3267e-05 lr: 3.0243e-06 eta: 14:23:27 time: 2.8228 data_time: 0.1827 memory: 14134 grad_norm: 0.6569 loss: 0.4530 detection_loss_cls: 0.4530 2024/07/14 21:41:06 - mmengine - INFO - Iter(train) [101650/120000] base_lr: 1.3207e-05 lr: 3.0188e-06 eta: 14:21:06 time: 2.8226 data_time: 0.1830 memory: 14134 grad_norm: 0.6555 loss: 0.4533 detection_loss_cls: 0.4533 2024/07/14 21:43:27 - mmengine - INFO - Iter(train) [101700/120000] base_lr: 1.3147e-05 lr: 3.0134e-06 eta: 14:18:45 time: 2.8223 data_time: 0.1829 memory: 14134 grad_norm: 0.6559 loss: 0.4531 detection_loss_cls: 0.4531 2024/07/14 21:45:46 - mmengine - INFO - Iter(train) [101750/120000] base_lr: 1.3088e-05 lr: 3.0080e-06 eta: 14:16:23 time: 2.8220 data_time: 0.1827 memory: 14134 grad_norm: 0.6562 loss: 0.4528 detection_loss_cls: 0.4528 2024/07/14 21:48:07 - mmengine - INFO - Iter(train) [101800/120000] base_lr: 1.3028e-05 lr: 3.0026e-06 eta: 14:14:02 time: 2.8220 data_time: 0.1824 memory: 14134 grad_norm: 0.6564 loss: 0.4527 detection_loss_cls: 0.4527 2024/07/14 21:50:26 - mmengine - INFO - Iter(train) [101850/120000] base_lr: 1.2969e-05 lr: 2.9972e-06 eta: 14:11:41 time: 2.8218 data_time: 0.1823 memory: 14134 grad_norm: 0.6569 loss: 0.4524 detection_loss_cls: 0.4524 2024/07/14 21:52:47 - mmengine - INFO - Iter(train) [101900/120000] base_lr: 1.2910e-05 lr: 2.9918e-06 eta: 14:09:20 time: 2.8220 data_time: 0.1823 memory: 14134 grad_norm: 0.6569 loss: 0.4526 detection_loss_cls: 0.4526 2024/07/14 21:55:08 - mmengine - INFO - Iter(train) [101950/120000] base_lr: 1.2850e-05 lr: 2.9864e-06 eta: 14:06:59 time: 2.8216 data_time: 0.1820 memory: 14134 grad_norm: 0.6569 loss: 0.4520 detection_loss_cls: 0.4520 2024/07/14 21:57:27 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 21:57:27 - mmengine - INFO - Iter(train) [102000/120000] base_lr: 1.2792e-05 lr: 2.9810e-06 eta: 14:04:37 time: 2.8213 data_time: 0.1821 memory: 14134 grad_norm: 0.6569 loss: 0.4520 detection_loss_cls: 0.4520 2024/07/14 21:57:27 - mmengine - INFO - Saving checkpoint at 102000 iterations 2024/07/14 21:59:55 - mmengine - INFO - Iter(train) [102050/120000] base_lr: 1.2733e-05 lr: 2.9757e-06 eta: 14:02:21 time: 2.8211 data_time: 0.1819 memory: 14134 grad_norm: 0.6569 loss: 0.4521 detection_loss_cls: 0.4521 2024/07/14 22:02:15 - mmengine - INFO - Iter(train) [102100/120000] base_lr: 1.2674e-05 lr: 2.9704e-06 eta: 13:59:59 time: 2.8207 data_time: 0.1819 memory: 14134 grad_norm: 0.6566 loss: 0.4521 detection_loss_cls: 0.4521 2024/07/14 22:04:35 - mmengine - INFO - Iter(train) [102150/120000] base_lr: 1.2616e-05 lr: 2.9651e-06 eta: 13:57:38 time: 2.8211 data_time: 0.1821 memory: 14134 grad_norm: 0.6565 loss: 0.4521 detection_loss_cls: 0.4521 2024/07/14 22:06:55 - mmengine - INFO - Iter(train) [102200/120000] base_lr: 1.2557e-05 lr: 2.9598e-06 eta: 13:55:17 time: 2.8208 data_time: 0.1817 memory: 14134 grad_norm: 0.6568 loss: 0.4513 detection_loss_cls: 0.4513 2024/07/14 22:09:14 - mmengine - INFO - Iter(train) [102250/120000] base_lr: 1.2499e-05 lr: 2.9545e-06 eta: 13:52:55 time: 2.8204 data_time: 0.1817 memory: 14134 grad_norm: 0.6569 loss: 0.4515 detection_loss_cls: 0.4515 2024/07/14 22:11:34 - mmengine - INFO - Iter(train) [102300/120000] base_lr: 1.2441e-05 lr: 2.9492e-06 eta: 13:50:34 time: 2.8201 data_time: 0.1815 memory: 14134 grad_norm: 0.6567 loss: 0.4513 detection_loss_cls: 0.4513 2024/07/14 22:13:53 - mmengine - INFO - Iter(train) [102350/120000] base_lr: 1.2383e-05 lr: 2.9439e-06 eta: 13:48:12 time: 2.8194 data_time: 0.1815 memory: 14134 grad_norm: 0.6569 loss: 0.4512 detection_loss_cls: 0.4512 2024/07/14 22:16:12 - mmengine - INFO - Iter(train) [102400/120000] base_lr: 1.2326e-05 lr: 2.9387e-06 eta: 13:45:50 time: 2.8192 data_time: 0.1815 memory: 14134 grad_norm: 0.6571 loss: 0.4510 detection_loss_cls: 0.4510 2024/07/14 22:18:31 - mmengine - INFO - Iter(train) [102450/120000] base_lr: 1.2268e-05 lr: 2.9335e-06 eta: 13:43:28 time: 2.8188 data_time: 0.1816 memory: 14134 grad_norm: 0.6570 loss: 0.4512 detection_loss_cls: 0.4512 2024/07/14 22:20:53 - mmengine - INFO - Iter(train) [102500/120000] base_lr: 1.2211e-05 lr: 2.9282e-06 eta: 13:41:08 time: 2.8188 data_time: 0.1815 memory: 14134 grad_norm: 0.6572 loss: 0.4513 detection_loss_cls: 0.4513 2024/07/14 22:23:14 - mmengine - INFO - Iter(train) [102550/120000] base_lr: 1.2154e-05 lr: 2.9230e-06 eta: 13:38:48 time: 2.8191 data_time: 0.1818 memory: 14134 grad_norm: 0.6573 loss: 0.4515 detection_loss_cls: 0.4515 2024/07/14 22:25:34 - mmengine - INFO - Iter(train) [102600/120000] base_lr: 1.2096e-05 lr: 2.9179e-06 eta: 13:36:26 time: 2.8187 data_time: 0.1815 memory: 14134 grad_norm: 0.6574 loss: 0.4511 detection_loss_cls: 0.4511 2024/07/14 22:27:54 - mmengine - INFO - Iter(train) [102650/120000] base_lr: 1.2039e-05 lr: 2.9127e-06 eta: 13:34:05 time: 2.8188 data_time: 0.1817 memory: 14134 grad_norm: 0.6578 loss: 0.4512 detection_loss_cls: 0.4512 2024/07/14 22:30:15 - mmengine - INFO - Iter(train) [102700/120000] base_lr: 1.1983e-05 lr: 2.9075e-06 eta: 13:31:44 time: 2.8187 data_time: 0.1815 memory: 14134 grad_norm: 0.6579 loss: 0.4509 detection_loss_cls: 0.4509 2024/07/14 22:32:35 - mmengine - INFO - Iter(train) [102750/120000] base_lr: 1.1926e-05 lr: 2.9024e-06 eta: 13:29:23 time: 2.8180 data_time: 0.1813 memory: 14134 grad_norm: 0.6579 loss: 0.4508 detection_loss_cls: 0.4508 2024/07/14 22:34:55 - mmengine - INFO - Iter(train) [102800/120000] base_lr: 1.1870e-05 lr: 2.8972e-06 eta: 13:27:02 time: 2.8180 data_time: 0.1815 memory: 14134 grad_norm: 0.6576 loss: 0.4508 detection_loss_cls: 0.4508 2024/07/14 22:37:15 - mmengine - INFO - Iter(train) [102850/120000] base_lr: 1.1813e-05 lr: 2.8921e-06 eta: 13:24:41 time: 2.8179 data_time: 0.1817 memory: 14134 grad_norm: 0.6577 loss: 0.4512 detection_loss_cls: 0.4512 2024/07/14 22:39:36 - mmengine - INFO - Iter(train) [102900/120000] base_lr: 1.1757e-05 lr: 2.8870e-06 eta: 13:22:20 time: 2.8179 data_time: 0.1823 memory: 14134 grad_norm: 0.6579 loss: 0.4517 detection_loss_cls: 0.4517 2024/07/14 22:41:57 - mmengine - INFO - Iter(train) [102950/120000] base_lr: 1.1701e-05 lr: 2.8819e-06 eta: 13:19:59 time: 2.8177 data_time: 0.1824 memory: 14134 grad_norm: 0.6578 loss: 0.4519 detection_loss_cls: 0.4519 2024/07/14 22:44:17 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 22:44:17 - mmengine - INFO - Iter(train) [103000/120000] base_lr: 1.1645e-05 lr: 2.8768e-06 eta: 13:17:38 time: 2.8179 data_time: 0.1825 memory: 14134 grad_norm: 0.6578 loss: 0.4521 detection_loss_cls: 0.4521 2024/07/14 22:44:17 - mmengine - INFO - Saving checkpoint at 103000 iterations 2024/07/14 22:46:44 - mmengine - INFO - Iter(train) [103050/120000] base_lr: 1.1589e-05 lr: 2.8718e-06 eta: 13:15:21 time: 2.8173 data_time: 0.1824 memory: 14134 grad_norm: 0.6579 loss: 0.4518 detection_loss_cls: 0.4518 2024/07/14 22:49:03 - mmengine - INFO - Iter(train) [103100/120000] base_lr: 1.1534e-05 lr: 2.8667e-06 eta: 13:13:00 time: 2.8170 data_time: 0.1821 memory: 14134 grad_norm: 0.6580 loss: 0.4511 detection_loss_cls: 0.4511 2024/07/14 22:51:24 - mmengine - INFO - Iter(train) [103150/120000] base_lr: 1.1478e-05 lr: 2.8617e-06 eta: 13:10:39 time: 2.8171 data_time: 0.1816 memory: 14134 grad_norm: 0.6581 loss: 0.4503 detection_loss_cls: 0.4503 2024/07/14 22:53:45 - mmengine - INFO - Iter(train) [103200/120000] base_lr: 1.1423e-05 lr: 2.8567e-06 eta: 13:08:18 time: 2.8172 data_time: 0.1822 memory: 14134 grad_norm: 0.6584 loss: 0.4511 detection_loss_cls: 0.4511 2024/07/14 22:56:04 - mmengine - INFO - Iter(train) [103250/120000] base_lr: 1.1368e-05 lr: 2.8516e-06 eta: 13:05:56 time: 2.8172 data_time: 0.1823 memory: 14134 grad_norm: 0.6584 loss: 0.4510 detection_loss_cls: 0.4510 2024/07/14 22:58:24 - mmengine - INFO - Iter(train) [103300/120000] base_lr: 1.1313e-05 lr: 2.8467e-06 eta: 13:03:35 time: 2.8169 data_time: 0.1820 memory: 14134 grad_norm: 0.6581 loss: 0.4499 detection_loss_cls: 0.4499 2024/07/14 23:00:44 - mmengine - INFO - Iter(train) [103350/120000] base_lr: 1.1258e-05 lr: 2.8417e-06 eta: 13:01:13 time: 2.8168 data_time: 0.1821 memory: 14134 grad_norm: 0.6583 loss: 0.4499 detection_loss_cls: 0.4499 2024/07/14 23:03:04 - mmengine - INFO - Iter(train) [103400/120000] base_lr: 1.1204e-05 lr: 2.8367e-06 eta: 12:58:52 time: 2.8165 data_time: 0.1821 memory: 14134 grad_norm: 0.6582 loss: 0.4497 detection_loss_cls: 0.4497 2024/07/14 23:05:24 - mmengine - INFO - Iter(train) [103450/120000] base_lr: 1.1149e-05 lr: 2.8317e-06 eta: 12:56:31 time: 2.8163 data_time: 0.1820 memory: 14134 grad_norm: 0.6581 loss: 0.4495 detection_loss_cls: 0.4495 2024/07/14 23:07:45 - mmengine - INFO - Iter(train) [103500/120000] base_lr: 1.1095e-05 lr: 2.8268e-06 eta: 12:54:10 time: 2.8165 data_time: 0.1817 memory: 14134 grad_norm: 0.6580 loss: 0.4490 detection_loss_cls: 0.4490 2024/07/14 23:10:04 - mmengine - INFO - Iter(train) [103550/120000] base_lr: 1.1041e-05 lr: 2.8219e-06 eta: 12:51:49 time: 2.8160 data_time: 0.1816 memory: 14134 grad_norm: 0.6579 loss: 0.4488 detection_loss_cls: 0.4488 2024/07/14 23:12:23 - mmengine - INFO - Iter(train) [103600/120000] base_lr: 1.0987e-05 lr: 2.8170e-06 eta: 12:49:27 time: 2.8157 data_time: 0.1811 memory: 14134 grad_norm: 0.6578 loss: 0.4482 detection_loss_cls: 0.4482 2024/07/14 23:14:42 - mmengine - INFO - Iter(train) [103650/120000] base_lr: 1.0933e-05 lr: 2.8121e-06 eta: 12:47:05 time: 2.8157 data_time: 0.1811 memory: 14134 grad_norm: 0.6577 loss: 0.4482 detection_loss_cls: 0.4482 2024/07/14 23:17:02 - mmengine - INFO - Iter(train) [103700/120000] base_lr: 1.0879e-05 lr: 2.8072e-06 eta: 12:44:44 time: 2.8159 data_time: 0.1814 memory: 14134 grad_norm: 0.6576 loss: 0.4490 detection_loss_cls: 0.4490 2024/07/14 23:19:21 - mmengine - INFO - Iter(train) [103750/120000] base_lr: 1.0826e-05 lr: 2.8023e-06 eta: 12:42:22 time: 2.8154 data_time: 0.1814 memory: 14134 grad_norm: 0.6577 loss: 0.4489 detection_loss_cls: 0.4489 2024/07/14 23:21:40 - mmengine - INFO - Iter(train) [103800/120000] base_lr: 1.0772e-05 lr: 2.7975e-06 eta: 12:40:01 time: 2.8149 data_time: 0.1815 memory: 14134 grad_norm: 0.6571 loss: 0.4489 detection_loss_cls: 0.4489 2024/07/14 23:23:59 - mmengine - INFO - Iter(train) [103850/120000] base_lr: 1.0719e-05 lr: 2.7926e-06 eta: 12:37:39 time: 2.8148 data_time: 0.1813 memory: 14134 grad_norm: 0.6571 loss: 0.4485 detection_loss_cls: 0.4485 2024/07/14 23:26:19 - mmengine - INFO - Iter(train) [103900/120000] base_lr: 1.0666e-05 lr: 2.7878e-06 eta: 12:35:18 time: 2.8148 data_time: 0.1813 memory: 14134 grad_norm: 0.6572 loss: 0.4481 detection_loss_cls: 0.4481 2024/07/14 23:28:38 - mmengine - INFO - Iter(train) [103950/120000] base_lr: 1.0613e-05 lr: 2.7830e-06 eta: 12:32:56 time: 2.8147 data_time: 0.1812 memory: 14134 grad_norm: 0.6575 loss: 0.4480 detection_loss_cls: 0.4480 2024/07/14 23:30:58 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/14 23:30:58 - mmengine - INFO - Iter(train) [104000/120000] base_lr: 1.0560e-05 lr: 2.7782e-06 eta: 12:30:35 time: 2.8147 data_time: 0.1808 memory: 14134 grad_norm: 0.6574 loss: 0.4476 detection_loss_cls: 0.4476 2024/07/14 23:30:58 - mmengine - INFO - Saving checkpoint at 104000 iterations 2024/07/14 23:33:27 - mmengine - INFO - Iter(train) [104050/120000] base_lr: 1.0507e-05 lr: 2.7734e-06 eta: 12:28:18 time: 2.8099 data_time: 0.1761 memory: 14134 grad_norm: 0.6578 loss: 0.4483 detection_loss_cls: 0.4483 2024/07/14 23:35:48 - mmengine - INFO - Iter(train) [104100/120000] base_lr: 1.0455e-05 lr: 2.7686e-06 eta: 12:25:58 time: 2.8102 data_time: 0.1764 memory: 14134 grad_norm: 0.6578 loss: 0.4487 detection_loss_cls: 0.4487 2024/07/14 23:38:09 - mmengine - INFO - Iter(train) [104150/120000] base_lr: 1.0403e-05 lr: 2.7639e-06 eta: 12:23:38 time: 2.8102 data_time: 0.1766 memory: 14134 grad_norm: 0.6578 loss: 0.4486 detection_loss_cls: 0.4486 2024/07/14 23:40:30 - mmengine - INFO - Iter(train) [104200/120000] base_lr: 1.0350e-05 lr: 2.7591e-06 eta: 12:21:17 time: 2.8104 data_time: 0.1765 memory: 14134 grad_norm: 0.6581 loss: 0.4483 detection_loss_cls: 0.4483 2024/07/14 23:42:50 - mmengine - INFO - Iter(train) [104250/120000] base_lr: 1.0298e-05 lr: 2.7544e-06 eta: 12:18:56 time: 2.8100 data_time: 0.1765 memory: 14134 grad_norm: 0.6582 loss: 0.4483 detection_loss_cls: 0.4483 2024/07/14 23:45:11 - mmengine - INFO - Iter(train) [104300/120000] base_lr: 1.0247e-05 lr: 2.7497e-06 eta: 12:16:35 time: 2.8101 data_time: 0.1768 memory: 14134 grad_norm: 0.6585 loss: 0.4488 detection_loss_cls: 0.4488 2024/07/14 23:47:32 - mmengine - INFO - Iter(train) [104350/120000] base_lr: 1.0195e-05 lr: 2.7450e-06 eta: 12:14:14 time: 2.8105 data_time: 0.1766 memory: 14134 grad_norm: 0.6586 loss: 0.4484 detection_loss_cls: 0.4484 2024/07/14 23:49:52 - mmengine - INFO - Iter(train) [104400/120000] base_lr: 1.0143e-05 lr: 2.7403e-06 eta: 12:11:53 time: 2.8105 data_time: 0.1764 memory: 14134 grad_norm: 0.6587 loss: 0.4482 detection_loss_cls: 0.4482 2024/07/14 23:52:12 - mmengine - INFO - Iter(train) [104450/120000] base_lr: 1.0092e-05 lr: 2.7356e-06 eta: 12:09:32 time: 2.8103 data_time: 0.1761 memory: 14134 grad_norm: 0.6589 loss: 0.4481 detection_loss_cls: 0.4481 2024/07/14 23:54:33 - mmengine - INFO - Iter(train) [104500/120000] base_lr: 1.0041e-05 lr: 2.7310e-06 eta: 12:07:11 time: 2.8107 data_time: 0.1762 memory: 14134 grad_norm: 0.6586 loss: 0.4484 detection_loss_cls: 0.4484 2024/07/14 23:56:53 - mmengine - INFO - Iter(train) [104550/120000] base_lr: 9.9896e-06 lr: 2.7263e-06 eta: 12:04:50 time: 2.8106 data_time: 0.1762 memory: 14134 grad_norm: 0.6588 loss: 0.4485 detection_loss_cls: 0.4485 2024/07/14 23:59:14 - mmengine - INFO - Iter(train) [104600/120000] base_lr: 9.9387e-06 lr: 2.7217e-06 eta: 12:02:30 time: 2.8105 data_time: 0.1763 memory: 14134 grad_norm: 0.6590 loss: 0.4484 detection_loss_cls: 0.4484 2024/07/15 00:01:35 - mmengine - INFO - Iter(train) [104650/120000] base_lr: 9.8879e-06 lr: 2.7171e-06 eta: 12:00:09 time: 2.8105 data_time: 0.1765 memory: 14134 grad_norm: 0.6597 loss: 0.4484 detection_loss_cls: 0.4484 2024/07/15 00:03:56 - mmengine - INFO - Iter(train) [104700/120000] base_lr: 9.8373e-06 lr: 2.7125e-06 eta: 11:57:48 time: 2.8106 data_time: 0.1763 memory: 14134 grad_norm: 0.6596 loss: 0.4477 detection_loss_cls: 0.4477 2024/07/15 00:06:16 - mmengine - INFO - Iter(train) [104750/120000] base_lr: 9.7869e-06 lr: 2.7079e-06 eta: 11:55:27 time: 2.8107 data_time: 0.1766 memory: 14134 grad_norm: 0.6596 loss: 0.4478 detection_loss_cls: 0.4478 2024/07/15 00:08:36 - mmengine - INFO - Iter(train) [104800/120000] base_lr: 9.7366e-06 lr: 2.7033e-06 eta: 11:53:06 time: 2.8107 data_time: 0.1769 memory: 14134 grad_norm: 0.6603 loss: 0.4484 detection_loss_cls: 0.4484 2024/07/15 00:10:57 - mmengine - INFO - Iter(train) [104850/120000] base_lr: 9.6864e-06 lr: 2.6988e-06 eta: 11:50:46 time: 2.8106 data_time: 0.1769 memory: 14134 grad_norm: 0.6604 loss: 0.4484 detection_loss_cls: 0.4484 2024/07/15 00:13:17 - mmengine - INFO - Iter(train) [104900/120000] base_lr: 9.6364e-06 lr: 2.6942e-06 eta: 11:48:24 time: 2.8105 data_time: 0.1767 memory: 14134 grad_norm: 0.6606 loss: 0.4484 detection_loss_cls: 0.4484 2024/07/15 00:15:38 - mmengine - INFO - Iter(train) [104950/120000] base_lr: 9.5866e-06 lr: 2.6897e-06 eta: 11:46:04 time: 2.8104 data_time: 0.1768 memory: 14134 grad_norm: 0.6608 loss: 0.4484 detection_loss_cls: 0.4484 2024/07/15 00:17:57 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/15 00:17:57 - mmengine - INFO - Iter(train) [105000/120000] base_lr: 9.5369e-06 lr: 2.6852e-06 eta: 11:43:42 time: 2.8102 data_time: 0.1767 memory: 14134 grad_norm: 0.6611 loss: 0.4480 detection_loss_cls: 0.4480 2024/07/15 00:17:57 - mmengine - INFO - Saving checkpoint at 105000 iterations 2024/07/15 00:18:26 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:23 time: 0.4086 data_time: 0.0048 memory: 5705 2024/07/15 00:18:46 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:01 time: 0.4087 data_time: 0.0048 memory: 5705 2024/07/15 00:19:07 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:43 time: 0.4089 data_time: 0.0048 memory: 5705 2024/07/15 00:19:28 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:22 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 00:19:49 - mmengine - INFO - Iter(val) [250/834] eta: 0:04:01 time: 0.4091 data_time: 0.0048 memory: 5705 2024/07/15 00:20:09 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:39 time: 0.4092 data_time: 0.0048 memory: 5705 2024/07/15 00:20:29 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:19 time: 0.4092 data_time: 0.0048 memory: 5705 2024/07/15 00:20:50 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:58 time: 0.4093 data_time: 0.0048 memory: 5705 2024/07/15 00:21:10 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:37 time: 0.4092 data_time: 0.0048 memory: 5705 2024/07/15 00:21:30 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:16 time: 0.4092 data_time: 0.0048 memory: 5705 2024/07/15 00:21:50 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4091 data_time: 0.0048 memory: 5705 2024/07/15 00:22:11 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:35 time: 0.4092 data_time: 0.0048 memory: 5705 2024/07/15 00:22:31 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4091 data_time: 0.0048 memory: 5705 2024/07/15 00:22:52 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 00:23:12 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 00:23:33 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 00:23:58 - mmengine - INFO - Evaluating bbox... 2024/07/15 00:24:23 - mmengine - INFO - bbox_mAP_copypaste: 0.458 0.624 0.499 0.267 0.499 0.628 2024/07/15 00:24:24 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4580 coco/bbox_mAP_50: 0.6240 coco/bbox_mAP_75: 0.4990 coco/bbox_mAP_s: 0.2670 coco/bbox_mAP_m: 0.4990 coco/bbox_mAP_l: 0.6280 data_time: 0.0048 time: 0.4092 2024/07/15 00:26:45 - mmengine - INFO - Iter(train) [105050/120000] base_lr: 9.4874e-06 lr: 2.6807e-06 eta: 11:41:40 time: 2.8176 data_time: 0.1839 memory: 14134 grad_norm: 0.6610 loss: 0.4479 detection_loss_cls: 0.4479 2024/07/15 00:29:05 - mmengine - INFO - Iter(train) [105100/120000] base_lr: 9.4380e-06 lr: 2.6762e-06 eta: 11:39:19 time: 2.8174 data_time: 0.1834 memory: 14134 grad_norm: 0.6611 loss: 0.4472 detection_loss_cls: 0.4472 2024/07/15 00:31:27 - mmengine - INFO - Iter(train) [105150/120000] base_lr: 9.3888e-06 lr: 2.6717e-06 eta: 11:36:59 time: 2.8179 data_time: 0.1837 memory: 14134 grad_norm: 0.6614 loss: 0.4474 detection_loss_cls: 0.4474 2024/07/15 00:33:46 - mmengine - INFO - Iter(train) [105200/120000] base_lr: 9.3398e-06 lr: 2.6673e-06 eta: 11:34:37 time: 2.8174 data_time: 0.1835 memory: 14134 grad_norm: 0.6615 loss: 0.4475 detection_loss_cls: 0.4475 2024/07/15 00:36:06 - mmengine - INFO - Iter(train) [105250/120000] base_lr: 9.2909e-06 lr: 2.6628e-06 eta: 11:32:16 time: 2.8175 data_time: 0.1836 memory: 14134 grad_norm: 0.6615 loss: 0.4478 detection_loss_cls: 0.4478 2024/07/15 00:38:25 - mmengine - INFO - Iter(train) [105300/120000] base_lr: 9.2422e-06 lr: 2.6584e-06 eta: 11:29:54 time: 2.8172 data_time: 0.1835 memory: 14134 grad_norm: 0.6617 loss: 0.4478 detection_loss_cls: 0.4478 2024/07/15 00:40:45 - mmengine - INFO - Iter(train) [105350/120000] base_lr: 9.1936e-06 lr: 2.6540e-06 eta: 11:27:33 time: 2.8171 data_time: 0.1833 memory: 14134 grad_norm: 0.6616 loss: 0.4474 detection_loss_cls: 0.4474 2024/07/15 00:43:04 - mmengine - INFO - Iter(train) [105400/120000] base_lr: 9.1452e-06 lr: 2.6496e-06 eta: 11:25:11 time: 2.8168 data_time: 0.1832 memory: 14134 grad_norm: 0.6619 loss: 0.4473 detection_loss_cls: 0.4473 2024/07/15 00:45:23 - mmengine - INFO - Iter(train) [105450/120000] base_lr: 9.0969e-06 lr: 2.6452e-06 eta: 11:22:50 time: 2.8166 data_time: 0.1828 memory: 14134 grad_norm: 0.6622 loss: 0.4464 detection_loss_cls: 0.4464 2024/07/15 00:47:43 - mmengine - INFO - Iter(train) [105500/120000] base_lr: 9.0488e-06 lr: 2.6408e-06 eta: 11:20:29 time: 2.8167 data_time: 0.1831 memory: 14134 grad_norm: 0.6623 loss: 0.4467 detection_loss_cls: 0.4467 2024/07/15 00:50:03 - mmengine - INFO - Iter(train) [105550/120000] base_lr: 9.0009e-06 lr: 2.6364e-06 eta: 11:18:07 time: 2.8167 data_time: 0.1830 memory: 14134 grad_norm: 0.6626 loss: 0.4459 detection_loss_cls: 0.4459 2024/07/15 00:52:21 - mmengine - INFO - Iter(train) [105600/120000] base_lr: 8.9531e-06 lr: 2.6321e-06 eta: 11:15:45 time: 2.8159 data_time: 0.1829 memory: 14134 grad_norm: 0.6625 loss: 0.4458 detection_loss_cls: 0.4458 2024/07/15 00:54:40 - mmengine - INFO - Iter(train) [105650/120000] base_lr: 8.9055e-06 lr: 2.6278e-06 eta: 11:13:23 time: 2.8157 data_time: 0.1827 memory: 14134 grad_norm: 0.6627 loss: 0.4454 detection_loss_cls: 0.4454 2024/07/15 00:57:00 - mmengine - INFO - Iter(train) [105700/120000] base_lr: 8.8580e-06 lr: 2.6235e-06 eta: 11:11:03 time: 2.8157 data_time: 0.1828 memory: 14134 grad_norm: 0.6626 loss: 0.4454 detection_loss_cls: 0.4454 2024/07/15 00:59:21 - mmengine - INFO - Iter(train) [105750/120000] base_lr: 8.8107e-06 lr: 2.6192e-06 eta: 11:08:42 time: 2.8159 data_time: 0.1828 memory: 14134 grad_norm: 0.6627 loss: 0.4454 detection_loss_cls: 0.4454 2024/07/15 01:01:40 - mmengine - INFO - Iter(train) [105800/120000] base_lr: 8.7635e-06 lr: 2.6149e-06 eta: 11:06:20 time: 2.8156 data_time: 0.1828 memory: 14134 grad_norm: 0.6625 loss: 0.4452 detection_loss_cls: 0.4452 2024/07/15 01:04:00 - mmengine - INFO - Iter(train) [105850/120000] base_lr: 8.7165e-06 lr: 2.6106e-06 eta: 11:03:59 time: 2.8157 data_time: 0.1829 memory: 14134 grad_norm: 0.6620 loss: 0.4452 detection_loss_cls: 0.4452 2024/07/15 01:06:20 - mmengine - INFO - Iter(train) [105900/120000] base_lr: 8.6697e-06 lr: 2.6063e-06 eta: 11:01:38 time: 2.8154 data_time: 0.1831 memory: 14134 grad_norm: 0.6621 loss: 0.4456 detection_loss_cls: 0.4456 2024/07/15 01:08:39 - mmengine - INFO - Iter(train) [105950/120000] base_lr: 8.6230e-06 lr: 2.6021e-06 eta: 10:59:16 time: 2.8151 data_time: 0.1831 memory: 14134 grad_norm: 0.6620 loss: 0.4459 detection_loss_cls: 0.4459 2024/07/15 01:10:58 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/15 01:10:58 - mmengine - INFO - Iter(train) [106000/120000] base_lr: 8.5765e-06 lr: 2.5979e-06 eta: 10:56:54 time: 2.8148 data_time: 0.1829 memory: 14134 grad_norm: 0.6620 loss: 0.4456 detection_loss_cls: 0.4456 2024/07/15 01:10:58 - mmengine - INFO - Saving checkpoint at 106000 iterations 2024/07/15 01:13:24 - mmengine - INFO - Iter(train) [106050/120000] base_lr: 8.5301e-06 lr: 2.5936e-06 eta: 10:54:36 time: 2.8146 data_time: 0.1828 memory: 14134 grad_norm: 0.6619 loss: 0.4455 detection_loss_cls: 0.4455 2024/07/15 01:15:44 - mmengine - INFO - Iter(train) [106100/120000] base_lr: 8.4839e-06 lr: 2.5894e-06 eta: 10:52:15 time: 2.8146 data_time: 0.1828 memory: 14134 grad_norm: 0.6621 loss: 0.4453 detection_loss_cls: 0.4453 2024/07/15 01:18:03 - mmengine - INFO - Iter(train) [106150/120000] base_lr: 8.4378e-06 lr: 2.5853e-06 eta: 10:49:54 time: 2.8144 data_time: 0.1826 memory: 14134 grad_norm: 0.6626 loss: 0.4451 detection_loss_cls: 0.4451 2024/07/15 01:20:22 - mmengine - INFO - Iter(train) [106200/120000] base_lr: 8.3920e-06 lr: 2.5811e-06 eta: 10:47:32 time: 2.8142 data_time: 0.1826 memory: 14134 grad_norm: 0.6626 loss: 0.4454 detection_loss_cls: 0.4454 2024/07/15 01:22:43 - mmengine - INFO - Iter(train) [106250/120000] base_lr: 8.3462e-06 lr: 2.5769e-06 eta: 10:45:11 time: 2.8145 data_time: 0.1825 memory: 14134 grad_norm: 0.6626 loss: 0.4455 detection_loss_cls: 0.4455 2024/07/15 01:25:02 - mmengine - INFO - Iter(train) [106300/120000] base_lr: 8.3007e-06 lr: 2.5728e-06 eta: 10:42:50 time: 2.8143 data_time: 0.1825 memory: 14134 grad_norm: 0.6627 loss: 0.4453 detection_loss_cls: 0.4453 2024/07/15 01:27:23 - mmengine - INFO - Iter(train) [106350/120000] base_lr: 8.2552e-06 lr: 2.5687e-06 eta: 10:40:29 time: 2.8148 data_time: 0.1826 memory: 14134 grad_norm: 0.6627 loss: 0.4453 detection_loss_cls: 0.4453 2024/07/15 01:29:43 - mmengine - INFO - Iter(train) [106400/120000] base_lr: 8.2100e-06 lr: 2.5645e-06 eta: 10:38:08 time: 2.8150 data_time: 0.1830 memory: 14134 grad_norm: 0.6629 loss: 0.4460 detection_loss_cls: 0.4460 2024/07/15 01:32:03 - mmengine - INFO - Iter(train) [106450/120000] base_lr: 8.1649e-06 lr: 2.5604e-06 eta: 10:35:47 time: 2.8152 data_time: 0.1828 memory: 14134 grad_norm: 0.6631 loss: 0.4455 detection_loss_cls: 0.4455 2024/07/15 01:34:23 - mmengine - INFO - Iter(train) [106500/120000] base_lr: 8.1200e-06 lr: 2.5564e-06 eta: 10:33:26 time: 2.8149 data_time: 0.1831 memory: 14134 grad_norm: 0.6632 loss: 0.4460 detection_loss_cls: 0.4460 2024/07/15 01:36:43 - mmengine - INFO - Iter(train) [106550/120000] base_lr: 8.0752e-06 lr: 2.5523e-06 eta: 10:31:05 time: 2.8146 data_time: 0.1829 memory: 14134 grad_norm: 0.6632 loss: 0.4459 detection_loss_cls: 0.4459 2024/07/15 01:39:03 - mmengine - INFO - Iter(train) [106600/120000] base_lr: 8.0306e-06 lr: 2.5482e-06 eta: 10:28:43 time: 2.8145 data_time: 0.1832 memory: 14134 grad_norm: 0.6634 loss: 0.4463 detection_loss_cls: 0.4463 2024/07/15 01:41:24 - mmengine - INFO - Iter(train) [106650/120000] base_lr: 7.9861e-06 lr: 2.5442e-06 eta: 10:26:23 time: 2.8147 data_time: 0.1831 memory: 14134 grad_norm: 0.6631 loss: 0.4464 detection_loss_cls: 0.4464 2024/07/15 01:43:45 - mmengine - INFO - Iter(train) [106700/120000] base_lr: 7.9418e-06 lr: 2.5402e-06 eta: 10:24:02 time: 2.8148 data_time: 0.1831 memory: 14134 grad_norm: 0.6631 loss: 0.4462 detection_loss_cls: 0.4462 2024/07/15 01:46:05 - mmengine - INFO - Iter(train) [106750/120000] base_lr: 7.8977e-06 lr: 2.5362e-06 eta: 10:21:41 time: 2.8149 data_time: 0.1835 memory: 14134 grad_norm: 0.6635 loss: 0.4469 detection_loss_cls: 0.4469 2024/07/15 01:48:24 - mmengine - INFO - Iter(train) [106800/120000] base_lr: 7.8537e-06 lr: 2.5322e-06 eta: 10:19:20 time: 2.8145 data_time: 0.1833 memory: 14134 grad_norm: 0.6634 loss: 0.4467 detection_loss_cls: 0.4467 2024/07/15 01:50:43 - mmengine - INFO - Iter(train) [106850/120000] base_lr: 7.8099e-06 lr: 2.5282e-06 eta: 10:16:58 time: 2.8142 data_time: 0.1830 memory: 14134 grad_norm: 0.6634 loss: 0.4457 detection_loss_cls: 0.4457 2024/07/15 01:53:04 - mmengine - INFO - Iter(train) [106900/120000] base_lr: 7.7662e-06 lr: 2.5242e-06 eta: 10:14:38 time: 2.8143 data_time: 0.1827 memory: 14134 grad_norm: 0.6635 loss: 0.4461 detection_loss_cls: 0.4461 2024/07/15 01:55:23 - mmengine - INFO - Iter(train) [106950/120000] base_lr: 7.7227e-06 lr: 2.5202e-06 eta: 10:12:16 time: 2.8138 data_time: 0.1826 memory: 14134 grad_norm: 0.6636 loss: 0.4463 detection_loss_cls: 0.4463 2024/07/15 01:57:42 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/15 01:57:42 - mmengine - INFO - Iter(train) [107000/120000] base_lr: 7.6794e-06 lr: 2.5163e-06 eta: 10:09:54 time: 2.8134 data_time: 0.1826 memory: 14134 grad_norm: 0.6636 loss: 0.4461 detection_loss_cls: 0.4461 2024/07/15 01:57:42 - mmengine - INFO - Saving checkpoint at 107000 iterations 2024/07/15 02:00:09 - mmengine - INFO - Iter(train) [107050/120000] base_lr: 7.6362e-06 lr: 2.5124e-06 eta: 10:07:36 time: 2.8135 data_time: 0.1825 memory: 14134 grad_norm: 0.6635 loss: 0.4456 detection_loss_cls: 0.4456 2024/07/15 02:02:28 - mmengine - INFO - Iter(train) [107100/120000] base_lr: 7.5932e-06 lr: 2.5085e-06 eta: 10:05:15 time: 2.8134 data_time: 0.1825 memory: 14134 grad_norm: 0.6638 loss: 0.4459 detection_loss_cls: 0.4459 2024/07/15 02:04:47 - mmengine - INFO - Iter(train) [107150/120000] base_lr: 7.5503e-06 lr: 2.5046e-06 eta: 10:02:53 time: 2.8131 data_time: 0.1827 memory: 14134 grad_norm: 0.6638 loss: 0.4464 detection_loss_cls: 0.4464 2024/07/15 02:07:06 - mmengine - INFO - Iter(train) [107200/120000] base_lr: 7.5076e-06 lr: 2.5007e-06 eta: 10:00:32 time: 2.8125 data_time: 0.1819 memory: 14134 grad_norm: 0.6638 loss: 0.4451 detection_loss_cls: 0.4451 2024/07/15 02:09:26 - mmengine - INFO - Iter(train) [107250/120000] base_lr: 7.4651e-06 lr: 2.4968e-06 eta: 9:58:11 time: 2.8127 data_time: 0.1817 memory: 14134 grad_norm: 0.6639 loss: 0.4449 detection_loss_cls: 0.4449 2024/07/15 02:11:46 - mmengine - INFO - Iter(train) [107300/120000] base_lr: 7.4227e-06 lr: 2.4930e-06 eta: 9:55:50 time: 2.8127 data_time: 0.1817 memory: 14134 grad_norm: 0.6639 loss: 0.4452 detection_loss_cls: 0.4452 2024/07/15 02:14:04 - mmengine - INFO - Iter(train) [107350/120000] base_lr: 7.3804e-06 lr: 2.4891e-06 eta: 9:53:28 time: 2.8124 data_time: 0.1816 memory: 14134 grad_norm: 0.6638 loss: 0.4455 detection_loss_cls: 0.4455 2024/07/15 02:16:25 - mmengine - INFO - Iter(train) [107400/120000] base_lr: 7.3384e-06 lr: 2.4853e-06 eta: 9:51:07 time: 2.8125 data_time: 0.1819 memory: 14134 grad_norm: 0.6636 loss: 0.4458 detection_loss_cls: 0.4458 2024/07/15 02:18:46 - mmengine - INFO - Iter(train) [107450/120000] base_lr: 7.2965e-06 lr: 2.4815e-06 eta: 9:48:47 time: 2.8128 data_time: 0.1822 memory: 14134 grad_norm: 0.6637 loss: 0.4462 detection_loss_cls: 0.4462 2024/07/15 02:21:07 - mmengine - INFO - Iter(train) [107500/120000] base_lr: 7.2548e-06 lr: 2.4777e-06 eta: 9:46:26 time: 2.8127 data_time: 0.1820 memory: 14134 grad_norm: 0.6640 loss: 0.4460 detection_loss_cls: 0.4460 2024/07/15 02:23:27 - mmengine - INFO - Iter(train) [107550/120000] base_lr: 7.2132e-06 lr: 2.4739e-06 eta: 9:44:05 time: 2.8130 data_time: 0.1820 memory: 14134 grad_norm: 0.6639 loss: 0.4460 detection_loss_cls: 0.4460 2024/07/15 02:25:48 - mmengine - INFO - Iter(train) [107600/120000] base_lr: 7.1718e-06 lr: 2.4702e-06 eta: 9:41:44 time: 2.8135 data_time: 0.1821 memory: 14134 grad_norm: 0.6638 loss: 0.4459 detection_loss_cls: 0.4459 2024/07/15 02:28:07 - mmengine - INFO - Iter(train) [107650/120000] base_lr: 7.1305e-06 lr: 2.4664e-06 eta: 9:39:23 time: 2.8135 data_time: 0.1817 memory: 14134 grad_norm: 0.6636 loss: 0.4452 detection_loss_cls: 0.4452 2024/07/15 02:30:27 - mmengine - INFO - Iter(train) [107700/120000] base_lr: 7.0894e-06 lr: 2.4627e-06 eta: 9:37:02 time: 2.8135 data_time: 0.1821 memory: 14134 grad_norm: 0.6636 loss: 0.4458 detection_loss_cls: 0.4458 2024/07/15 02:32:45 - mmengine - INFO - Iter(train) [107750/120000] base_lr: 7.0485e-06 lr: 2.4590e-06 eta: 9:34:40 time: 2.8134 data_time: 0.1820 memory: 14134 grad_norm: 0.6635 loss: 0.4459 detection_loss_cls: 0.4459 2024/07/15 02:35:05 - mmengine - INFO - Iter(train) [107800/120000] base_lr: 7.0077e-06 lr: 2.4552e-06 eta: 9:32:19 time: 2.8135 data_time: 0.1818 memory: 14134 grad_norm: 0.6642 loss: 0.4456 detection_loss_cls: 0.4456 2024/07/15 02:37:26 - mmengine - INFO - Iter(train) [107850/120000] base_lr: 6.9671e-06 lr: 2.4516e-06 eta: 9:29:58 time: 2.8139 data_time: 0.1819 memory: 14134 grad_norm: 0.6640 loss: 0.4459 detection_loss_cls: 0.4459 2024/07/15 02:39:46 - mmengine - INFO - Iter(train) [107900/120000] base_lr: 6.9266e-06 lr: 2.4479e-06 eta: 9:27:37 time: 2.8140 data_time: 0.1820 memory: 14134 grad_norm: 0.6645 loss: 0.4465 detection_loss_cls: 0.4465 2024/07/15 02:42:06 - mmengine - INFO - Iter(train) [107950/120000] base_lr: 6.8863e-06 lr: 2.4442e-06 eta: 9:25:16 time: 2.8141 data_time: 0.1819 memory: 14134 grad_norm: 0.6645 loss: 0.4464 detection_loss_cls: 0.4464 2024/07/15 02:44:26 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/15 02:44:26 - mmengine - INFO - Iter(train) [108000/120000] base_lr: 6.8462e-06 lr: 2.4406e-06 eta: 9:22:55 time: 2.8141 data_time: 0.1819 memory: 14134 grad_norm: 0.6643 loss: 0.4462 detection_loss_cls: 0.4462 2024/07/15 02:44:26 - mmengine - INFO - Saving checkpoint at 108000 iterations 2024/07/15 02:46:54 - mmengine - INFO - Iter(train) [108050/120000] base_lr: 6.8062e-06 lr: 2.4369e-06 eta: 9:20:37 time: 2.8141 data_time: 0.1817 memory: 14134 grad_norm: 0.6639 loss: 0.4452 detection_loss_cls: 0.4452 2024/07/15 02:49:14 - mmengine - INFO - Iter(train) [108100/120000] base_lr: 6.7664e-06 lr: 2.4333e-06 eta: 9:18:16 time: 2.8136 data_time: 0.1816 memory: 14134 grad_norm: 0.6640 loss: 0.4453 detection_loss_cls: 0.4453 2024/07/15 02:51:33 - mmengine - INFO - Iter(train) [108150/120000] base_lr: 6.7268e-06 lr: 2.4297e-06 eta: 9:15:55 time: 2.8132 data_time: 0.1814 memory: 14134 grad_norm: 0.6641 loss: 0.4449 detection_loss_cls: 0.4449 2024/07/15 02:53:53 - mmengine - INFO - Iter(train) [108200/120000] base_lr: 6.6873e-06 lr: 2.4261e-06 eta: 9:13:34 time: 2.8129 data_time: 0.1818 memory: 14134 grad_norm: 0.6640 loss: 0.4455 detection_loss_cls: 0.4455 2024/07/15 02:56:12 - mmengine - INFO - Iter(train) [108250/120000] base_lr: 6.6480e-06 lr: 2.4225e-06 eta: 9:11:13 time: 2.8128 data_time: 0.1817 memory: 14134 grad_norm: 0.6638 loss: 0.4455 detection_loss_cls: 0.4455 2024/07/15 02:58:31 - mmengine - INFO - Iter(train) [108300/120000] base_lr: 6.6088e-06 lr: 2.4190e-06 eta: 9:08:51 time: 2.8125 data_time: 0.1816 memory: 14134 grad_norm: 0.6635 loss: 0.4451 detection_loss_cls: 0.4451 2024/07/15 03:00:52 - mmengine - INFO - Iter(train) [108350/120000] base_lr: 6.5698e-06 lr: 2.4154e-06 eta: 9:06:31 time: 2.8124 data_time: 0.1816 memory: 14134 grad_norm: 0.6637 loss: 0.4451 detection_loss_cls: 0.4451 2024/07/15 03:03:11 - mmengine - INFO - Iter(train) [108400/120000] base_lr: 6.5310e-06 lr: 2.4119e-06 eta: 9:04:09 time: 2.8119 data_time: 0.1819 memory: 14134 grad_norm: 0.6635 loss: 0.4455 detection_loss_cls: 0.4455 2024/07/15 03:05:31 - mmengine - INFO - Iter(train) [108450/120000] base_lr: 6.4923e-06 lr: 2.4084e-06 eta: 9:01:48 time: 2.8120 data_time: 0.1823 memory: 14134 grad_norm: 0.6638 loss: 0.4457 detection_loss_cls: 0.4457 2024/07/15 03:07:50 - mmengine - INFO - Iter(train) [108500/120000] base_lr: 6.4538e-06 lr: 2.4049e-06 eta: 8:59:27 time: 2.8115 data_time: 0.1822 memory: 14134 grad_norm: 0.6641 loss: 0.4456 detection_loss_cls: 0.4456 2024/07/15 03:10:10 - mmengine - INFO - Iter(train) [108550/120000] base_lr: 6.4154e-06 lr: 2.4014e-06 eta: 8:57:06 time: 2.8115 data_time: 0.1823 memory: 14134 grad_norm: 0.6639 loss: 0.4456 detection_loss_cls: 0.4456 2024/07/15 03:12:29 - mmengine - INFO - Iter(train) [108600/120000] base_lr: 6.3773e-06 lr: 2.3979e-06 eta: 8:54:45 time: 2.8110 data_time: 0.1822 memory: 14134 grad_norm: 0.6639 loss: 0.4456 detection_loss_cls: 0.4456 2024/07/15 03:14:49 - mmengine - INFO - Iter(train) [108650/120000] base_lr: 6.3392e-06 lr: 2.3945e-06 eta: 8:52:24 time: 2.8108 data_time: 0.1822 memory: 14134 grad_norm: 0.6636 loss: 0.4458 detection_loss_cls: 0.4458 2024/07/15 03:17:09 - mmengine - INFO - Iter(train) [108700/120000] base_lr: 6.3014e-06 lr: 2.3910e-06 eta: 8:50:03 time: 2.8105 data_time: 0.1821 memory: 14134 grad_norm: 0.6637 loss: 0.4458 detection_loss_cls: 0.4458 2024/07/15 03:19:29 - mmengine - INFO - Iter(train) [108750/120000] base_lr: 6.2637e-06 lr: 2.3876e-06 eta: 8:47:42 time: 2.8105 data_time: 0.1819 memory: 14134 grad_norm: 0.6635 loss: 0.4455 detection_loss_cls: 0.4455 2024/07/15 03:21:49 - mmengine - INFO - Iter(train) [108800/120000] base_lr: 6.2261e-06 lr: 2.3842e-06 eta: 8:45:21 time: 2.8104 data_time: 0.1819 memory: 14134 grad_norm: 0.6632 loss: 0.4455 detection_loss_cls: 0.4455 2024/07/15 03:24:09 - mmengine - INFO - Iter(train) [108850/120000] base_lr: 6.1887e-06 lr: 2.3808e-06 eta: 8:43:00 time: 2.8103 data_time: 0.1819 memory: 14134 grad_norm: 0.6641 loss: 0.4453 detection_loss_cls: 0.4453 2024/07/15 03:26:29 - mmengine - INFO - Iter(train) [108900/120000] base_lr: 6.1515e-06 lr: 2.3774e-06 eta: 8:40:39 time: 2.8102 data_time: 0.1818 memory: 14134 grad_norm: 0.6643 loss: 0.4449 detection_loss_cls: 0.4449 2024/07/15 03:28:50 - mmengine - INFO - Iter(train) [108950/120000] base_lr: 6.1145e-06 lr: 2.3740e-06 eta: 8:38:18 time: 2.8103 data_time: 0.1820 memory: 14134 grad_norm: 0.6646 loss: 0.4452 detection_loss_cls: 0.4452 2024/07/15 03:31:08 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/15 03:31:08 - mmengine - INFO - Iter(train) [109000/120000] base_lr: 6.0776e-06 lr: 2.3707e-06 eta: 8:35:57 time: 2.8100 data_time: 0.1820 memory: 14134 grad_norm: 0.6648 loss: 0.4450 detection_loss_cls: 0.4450 2024/07/15 03:31:08 - mmengine - INFO - Saving checkpoint at 109000 iterations 2024/07/15 03:33:35 - mmengine - INFO - Iter(train) [109050/120000] base_lr: 6.0409e-06 lr: 2.3674e-06 eta: 8:33:38 time: 2.8020 data_time: 0.1744 memory: 14134 grad_norm: 0.6645 loss: 0.4445 detection_loss_cls: 0.4445 2024/07/15 03:35:55 - mmengine - INFO - Iter(train) [109100/120000] base_lr: 6.0043e-06 lr: 2.3640e-06 eta: 8:31:17 time: 2.8021 data_time: 0.1743 memory: 14134 grad_norm: 0.6645 loss: 0.4444 detection_loss_cls: 0.4444 2024/07/15 03:38:15 - mmengine - INFO - Iter(train) [109150/120000] base_lr: 5.9679e-06 lr: 2.3607e-06 eta: 8:28:56 time: 2.8016 data_time: 0.1739 memory: 14134 grad_norm: 0.6643 loss: 0.4441 detection_loss_cls: 0.4441 2024/07/15 03:40:34 - mmengine - INFO - Iter(train) [109200/120000] base_lr: 5.9316e-06 lr: 2.3574e-06 eta: 8:26:35 time: 2.8016 data_time: 0.1739 memory: 14134 grad_norm: 0.6644 loss: 0.4444 detection_loss_cls: 0.4444 2024/07/15 03:42:55 - mmengine - INFO - Iter(train) [109250/120000] base_lr: 5.8956e-06 lr: 2.3541e-06 eta: 8:24:14 time: 2.8019 data_time: 0.1737 memory: 14134 grad_norm: 0.6643 loss: 0.4442 detection_loss_cls: 0.4442 2024/07/15 03:45:16 - mmengine - INFO - Iter(train) [109300/120000] base_lr: 5.8597e-06 lr: 2.3509e-06 eta: 8:21:53 time: 2.8022 data_time: 0.1737 memory: 14134 grad_norm: 0.6645 loss: 0.4440 detection_loss_cls: 0.4440 2024/07/15 03:47:35 - mmengine - INFO - Iter(train) [109350/120000] base_lr: 5.8239e-06 lr: 2.3476e-06 eta: 8:19:32 time: 2.8021 data_time: 0.1739 memory: 14134 grad_norm: 0.6646 loss: 0.4440 detection_loss_cls: 0.4440 2024/07/15 03:49:56 - mmengine - INFO - Iter(train) [109400/120000] base_lr: 5.7883e-06 lr: 2.3444e-06 eta: 8:17:11 time: 2.8026 data_time: 0.1742 memory: 14134 grad_norm: 0.6648 loss: 0.4446 detection_loss_cls: 0.4446 2024/07/15 03:52:15 - mmengine - INFO - Iter(train) [109450/120000] base_lr: 5.7529e-06 lr: 2.3412e-06 eta: 8:14:50 time: 2.8026 data_time: 0.1747 memory: 14134 grad_norm: 0.6648 loss: 0.4458 detection_loss_cls: 0.4458 2024/07/15 03:54:35 - mmengine - INFO - Iter(train) [109500/120000] base_lr: 5.7176e-06 lr: 2.3380e-06 eta: 8:12:29 time: 2.8025 data_time: 0.1747 memory: 14134 grad_norm: 0.6648 loss: 0.4458 detection_loss_cls: 0.4458 2024/07/15 03:56:55 - mmengine - INFO - Iter(train) [109550/120000] base_lr: 5.6825e-06 lr: 2.3348e-06 eta: 8:10:08 time: 2.8026 data_time: 0.1748 memory: 14134 grad_norm: 0.6646 loss: 0.4464 detection_loss_cls: 0.4464 2024/07/15 03:59:14 - mmengine - INFO - Iter(train) [109600/120000] base_lr: 5.6476e-06 lr: 2.3316e-06 eta: 8:07:47 time: 2.8030 data_time: 0.1748 memory: 14134 grad_norm: 0.6646 loss: 0.4464 detection_loss_cls: 0.4464 2024/07/15 04:01:33 - mmengine - INFO - Iter(train) [109650/120000] base_lr: 5.6128e-06 lr: 2.3284e-06 eta: 8:05:26 time: 2.8030 data_time: 0.1751 memory: 14134 grad_norm: 0.6643 loss: 0.4469 detection_loss_cls: 0.4469 2024/07/15 04:03:54 - mmengine - INFO - Iter(train) [109700/120000] base_lr: 5.5782e-06 lr: 2.3253e-06 eta: 8:03:05 time: 2.8030 data_time: 0.1751 memory: 14134 grad_norm: 0.6647 loss: 0.4472 detection_loss_cls: 0.4472 2024/07/15 04:06:13 - mmengine - INFO - Iter(train) [109750/120000] base_lr: 5.5438e-06 lr: 2.3222e-06 eta: 8:00:44 time: 2.8027 data_time: 0.1753 memory: 14134 grad_norm: 0.6646 loss: 0.4473 detection_loss_cls: 0.4473 2024/07/15 04:08:32 - mmengine - INFO - Iter(train) [109800/120000] base_lr: 5.5095e-06 lr: 2.3190e-06 eta: 7:58:23 time: 2.8025 data_time: 0.1754 memory: 14134 grad_norm: 0.6649 loss: 0.4476 detection_loss_cls: 0.4476 2024/07/15 04:10:52 - mmengine - INFO - Iter(train) [109850/120000] base_lr: 5.4754e-06 lr: 2.3159e-06 eta: 7:56:02 time: 2.8024 data_time: 0.1753 memory: 14134 grad_norm: 0.6652 loss: 0.4474 detection_loss_cls: 0.4474 2024/07/15 04:13:11 - mmengine - INFO - Iter(train) [109900/120000] base_lr: 5.4414e-06 lr: 2.3129e-06 eta: 7:53:41 time: 2.8024 data_time: 0.1751 memory: 14134 grad_norm: 0.6653 loss: 0.4470 detection_loss_cls: 0.4470 2024/07/15 04:15:30 - mmengine - INFO - Iter(train) [109950/120000] base_lr: 5.4076e-06 lr: 2.3098e-06 eta: 7:51:20 time: 2.8024 data_time: 0.1749 memory: 14134 grad_norm: 0.6652 loss: 0.4467 detection_loss_cls: 0.4467 2024/07/15 04:17:49 - mmengine - INFO - Exp name: single_detection_base_1120_prompt_beta_20240714_001851 2024/07/15 04:17:49 - mmengine - INFO - Iter(train) [110000/120000] base_lr: 5.3740e-06 lr: 2.3067e-06 eta: 7:48:58 time: 2.8025 data_time: 0.1749 memory: 14134 grad_norm: 0.6652 loss: 0.4466 detection_loss_cls: 0.4466 2024/07/15 04:17:49 - mmengine - INFO - Saving checkpoint at 110000 iterations 2024/07/15 04:18:18 - mmengine - INFO - Iter(val) [ 50/834] eta: 0:05:22 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 04:18:38 - mmengine - INFO - Iter(val) [100/834] eta: 0:05:00 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 04:18:58 - mmengine - INFO - Iter(val) [150/834] eta: 0:04:40 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 04:19:19 - mmengine - INFO - Iter(val) [200/834] eta: 0:04:19 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 04:19:39 - mmengine - INFO - Iter(val) [250/834] eta: 0:03:59 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 04:20:00 - mmengine - INFO - Iter(val) [300/834] eta: 0:03:38 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 04:20:20 - mmengine - INFO - Iter(val) [350/834] eta: 0:03:17 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 04:20:41 - mmengine - INFO - Iter(val) [400/834] eta: 0:02:57 time: 0.4092 data_time: 0.0048 memory: 5705 2024/07/15 04:21:01 - mmengine - INFO - Iter(val) [450/834] eta: 0:02:37 time: 0.4091 data_time: 0.0048 memory: 5705 2024/07/15 04:21:22 - mmengine - INFO - Iter(val) [500/834] eta: 0:02:16 time: 0.4091 data_time: 0.0048 memory: 5705 2024/07/15 04:21:42 - mmengine - INFO - Iter(val) [550/834] eta: 0:01:56 time: 0.4091 data_time: 0.0048 memory: 5705 2024/07/15 04:22:03 - mmengine - INFO - Iter(val) [600/834] eta: 0:01:35 time: 0.4091 data_time: 0.0048 memory: 5705 2024/07/15 04:22:23 - mmengine - INFO - Iter(val) [650/834] eta: 0:01:15 time: 0.4091 data_time: 0.0048 memory: 5705 2024/07/15 04:22:43 - mmengine - INFO - Iter(val) [700/834] eta: 0:00:54 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 04:23:04 - mmengine - INFO - Iter(val) [750/834] eta: 0:00:34 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 04:23:24 - mmengine - INFO - Iter(val) [800/834] eta: 0:00:13 time: 0.4090 data_time: 0.0048 memory: 5705 2024/07/15 04:23:40 - mmengine - INFO - Evaluating bbox... 2024/07/15 04:24:05 - mmengine - INFO - bbox_mAP_copypaste: 0.458 0.625 0.499 0.267 0.500 0.627 2024/07/15 04:24:06 - mmengine - INFO - Iter(val) [834/834] coco/bbox_mAP: 0.4580 coco/bbox_mAP_50: 0.6250 coco/bbox_mAP_75: 0.4990 coco/bbox_mAP_s: 0.2670 coco/bbox_mAP_m: 0.5000 coco/bbox_mAP_l: 0.6270 data_time: 0.0048 time: 0.4088 2024/07/15 04:26:26 - mmengine - INFO - Iter(train) [110050/120000] base_lr: 5.3406e-06 lr: 2.3037e-06 eta: 7:46:45 time: 2.8076 data_time: 0.1800 memory: 14134 grad_norm: 0.6655 loss: 0.4463 detection_loss_cls: 0.4463 2024/07/15 04:28:46 - mmengine - INFO - Iter(train) [110100/120000] base_lr: 5.3073e-06 lr: 2.3007e-06 eta: 7:44:25 time: 2.8080 data_time: 0.1797 memory: 14134 grad_norm: 0.6654 loss: 0.4458 detection_loss_cls: 0.4458 2024/07/15 04:31:07 - mmengine - INFO - Iter(train) [110150/120000] base_lr: 5.2741e-06 lr: 2.2976e-06 eta: 7:42:04 time: 2.8083 data_time: 0.1800 memory: 14134 grad_norm: 0.6652 loss: 0.4463 detection_loss_cls: 0.4463 2024/07/15 04:33:28 - mmengine - INFO - Iter(train) [110200/120000] base_lr: 5.2412e-06 lr: 2.2947e-06 eta: 7:39:43 time: 2.8088 data_time: 0.1801 memory: 14134 grad_norm: 0.6650 loss: 0.4461 detection_loss_cls: 0.4461 2024/07/15 04:35:48 - mmengine - INFO - Iter(train) [110250/120000] base_lr: 5.2083e-06 lr: 2.2917e-06 eta: 7:37:22 time: 2.8086 data_time: 0.1800 memory: 14134 grad_norm: 0.6648 loss: 0.4458 detection_loss_cls: 0.4458 2024/07/15 04:38:09 - mmengine - INFO - Iter(train) [110300/120000] base_lr: 5.1757e-06 lr: 2.2887e-06 eta: 7:35:02 time: 2.8091 data_time: 0.1805 memory: 14134 grad_norm: 0.6647 loss: 0.4467 detection_loss_cls: 0.4467